Treatment space mapping method

Abstract

A treatment space mapping method is disclosed, the method comprising: a. Delivering stimulation at at least one location within the brain using at least one combination of treatment parameter values, receiving a plurality of signals related to a patient condition from at least one sensor during and / or after at least one brain stimulation period; b. Analysing the received plurality of signals to assess a certain amount of the at least one therapeutic side effect and the at least one symptom effect; and c. Mapping the treatment space based on the quantitative evaluation.

Description

[0001] This application is a divisional application of application number 201980071581.9 (PCT application number is PCT / IB2019 / 057524), application date September 6, 2019, and invention name “Therapeutic Space Assessment”.

[0002] Related Applications

[0003] Pursuant to 35 U.S.C. § 119(e) of the United States Patent Act, this application claims priority to U.S. Provisional Application No. 62 / 727,641, filed on September 6, 2018, the disclosure of which is incorporated herein by reference.

[0004] Technical field and background technology

[0005] The present invention, in some embodiments thereof, relates to treatment space assessment, and more particularly, but not limited to, treatment space assessment for a brain stimulation therapy.

[0006] Movement disorders can be defined as neurological disorders that affect the speed, fluency, quality, and comfort of movement and can be caused by genetic, acquired, or idiopathic causes. In some movement disorders, such as Parkinson's disease, there are additional signs and symptoms to watch out for, the assessment of which is important for diagnosis and assessment of the severity of the disease.

[0007] Evaluation of movement disorder symptoms and signs is important during diagnosis, treatment, and post-treatment of the disease.

[0008] Here are some attempts to assess movement disorders and their symptoms: “A novel adjunctive method for rigidity assessment during deep brain stimulation surgery using accelerometers” by Ashesh Shah et al., “A portable system for quantitative assessment of rigidity in Atkinson’s disease” by Houde Dai et al., “A new method for systematic analysis of rigidity in Parkinson’s disease” by Takayuki Endo et al., “Measurement of rigidity in Parkinson’s disease” by Arthur Prochazka et al., “Quantification of hand motor symptoms in Parkinson’s disease: a proof-of-principle study using inertial and force sensors” by JOSIEN C. VAN DEN NOORT et al., “Research and development of a portable device to quantify muscle tone in patients with Parkinson’s disease” by David Wright et al., “QAPD: an integrated system for quantifying symptoms of Parkinson’s disease” by Vajeshri Patel et al., “Assessment of bradykinesia in patients with Parkinson’s disease using gyroscope signals” by S. Summa et al., “An adaptive model approach for quantitative wrist rigidity assessment during deep brain stimulation surgery” by Sofia Assis et al., and “An adaptive model approach for quantitative wrist rigidity assessment during deep brain stimulation surgery” by Di “Mobile cloud-based home monitoring and assessment system for Parkinson’s disease” by Pan et al.

[0009] Other background art includes US Patent Publication No. US9,289,603 and US Patent Publication No. US9,282,928. Summary of the invention

[0010] Some examples of some embodiments of the present invention are listed below:

[0011] Embodiment 1: A method for mapping a treatment space, the method comprising: a. delivering stimulation at at least one location within the brain using at least one combination of treatment parameter values, receiving multiple signals related to a patient condition from at least one sensor during and / or after at least one brain stimulation period; b. analyzing the received multiple signals to perform a quantitative assessment of at least one treatment side effect and at least one symptomatic effect; and c. mapping the treatment space based on the quantitative assessment.

[0012] Embodiment 2: The method according to embodiment 1, wherein the mapping includes mapping the treatment space based on a desired future flexibility.

[0013] Embodiment 3: According to the method described in Embodiment 1, the method further comprises recording the multiple signals when the patient is resting.

[0014] Embodiment 4: According to the method of embodiment 1, the method further comprises recording the multiple signals when the patient performs a task.

[0015] Embodiment 5: According to the method described in Embodiment 1, the method further includes determining that a stimulation electrode or an electrode lead is located at a correct position in the brain.

[0016] Embodiment 6: According to the method of embodiment 1, at least one combination of treatment parameter values ​​is selected based on the mapping of the treatment space.

[0017] Embodiment 7: According to the method of embodiment 1, the method further includes transmitting an indication about the treatment space.

[0018] Embodiment 8: According to the method of embodiment 1, the received multiple signals are measured during a deep brain stimulation (DBS) lead implantation surgery in an operating room and / or after at least one brain stimulation in the operating room.

[0019] Embodiment 9: The method according to embodiment 1, wherein the analysis is based on stored continuously measured signals.

[0020] Embodiment 10: The method according to embodiment 1 further comprises selecting at least one combination of treatment parameter values ​​based on a quantitative assessment of the treatment side effects and the at least one symptomatic effect measured during the electrode lead implantation surgery.

[0021] Embodiment 11: The method according to embodiment 1 further comprises transmitting an indication of the at least one treatment side effect and the at least one symptomatic effect through a user interface.

[0022] Embodiment 12: The method according to embodiment 2 further comprises calculating at least one value of the future flexibility based on the quantitative assessment of the combination of the at least one treatment side effect and the at least one symptomatic effect and / or the at least one treatment parameter value.

[0023] Embodiment 13: The method according to embodiment 12 further comprises generating a treatment space based on the at least one future flexibility value, the quantitative assessment of the at least one side effect and the at least one therapeutic side effect, and the at least one treatment parameter value combination for the at least one stimulation.

[0024] Embodiment 14: According to the method described in Embodiment 1, the method further includes transmitting a signal to a user interface to convey an indication of the mapped treatment space, wherein the treatment space is a multidimensional space defined by two or more treatment parameter values, and the treatment parameter values ​​promote a desired treatment effect and a desired side effect level.

[0025] Embodiment 15: According to the method of embodiment 14, the method further comprises calculating at least one optional combination of treatment parameter values ​​based on the mapped treatment space.

[0026] Embodiment 16: The method according to embodiment 15 further comprises sending a signal to the user interface to convey an indication related to the at least one selectable combination of treatment parameter values.

[0027] Embodiment 17: The method according to embodiment 14 further comprises calculating a relationship between at least one combination of treatment parameter values ​​and the mapped treatment space, and sending a signal to the user interface to transmit an indication of the relationship.

[0028] Embodiment 18: The method of embodiment 11, wherein the mapping is based on at least one desired future flexibility range or score.

[0029] Example 19: According to the method described in Example 18, the method further includes calculating the at least one numerical value of the future flexibility based on a future effect of at least one treatment effect modifier, and the at least one treatment effect modifier includes disease progression, future variables of treatment side effects, future variables of disease symptoms, future variables of stimulation locations, future variables of the number and / or combination of stimulation electrodes, and drug usage patterns.

[0030] Embodiment 20: The method of embodiment 1, further comprising generating a graphical representation of a level of future effect of the at least one therapeutic effect modifier.

[0031] Embodiment 21: The method according to embodiment 20 further comprises receiving at least one value related to the future flexibility from a remote database.

[0032] Embodiment 22: According to the method of embodiment 20, at least one value related to the future flexibility is calculated based on a large data set collected from multiple patients.

[0033] Embodiment 23: The method of embodiment 1, further comprising displaying a list of multiple treatment parameter value combinations suitable for delivering brain stimulation based on the at least one treatment side effect and the at least one symptomatic effect.

[0034] Embodiment 24: The method of embodiment 1, further comprising displaying a graphical representation of the generated treatment space around the at least one combination of treatment parameter values ​​for the at least one stimulus.

[0035] Embodiment 25: The method according to embodiment 1 further comprises generating a score for each of a plurality of therapeutic effect modifiers, and further comprises displaying a graphical representation of the generated score associated with the therapeutic effect modifier.

[0036] Embodiment 26: The method of Embodiment 1, wherein the at least one treatment side effect comprises gaze deviation, diplopia, persistent activation of leg, arm or facial muscles, and movement disorders.

[0037] Embodiment 27: The method of Embodiment 1, wherein the at least one symptomatic effect comprises one or more of muscle rigidity, tremor, and bradykinesia.

[0038] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as those generally understood by those of ordinary skill in the art to which the present invention belongs. Although methods and materials similar or equivalent to the methods and materials described herein may be used in the implementation or testing of the embodiments of the present invention, the methods and / or materials described below are exemplary. In the event of a conflict, the scope of the claims including their definitions shall prevail. In addition, these materials, methods and embodiments are illustrative only and are not intended to be limiting.

[0039] As will be appreciated by those skilled in the art, some embodiments of the present invention may be implemented as a system, method or computer program product. Therefore, some embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment (including firmware, resident software, microcode, etc.) or a combination of software and hardware embodiments, which may generally be referred to herein as "circuit", "module" or "system". In addition, some embodiments of the present invention may take the form of a computer program product embodied in one or more computer-readable media, the computer-readable medium having a computer-readable program code contained thereon. The implementation of the method and / or system of some embodiments of the present invention may involve manual, automatic or a combination thereof to perform and / or complete selected tasks. In addition, according to the actual instruments and equipment of some embodiments of the method and / or system of the present invention, several selected tasks may be implemented by hardware, software or firmware and / or a combination thereof, for example, using an operating system.

[0040] For example, the hardware for performing selected tasks according to some embodiments of the present invention can be implemented as a chip or circuit. As software, the selected tasks according to some embodiments of the present invention can be implemented as a plurality of software instructions executed by a computer using any suitable operating system. In an exemplary embodiment of the present invention, one or more tasks according to some exemplary embodiments of the method and / or system described herein are performed by a data processor, such as a computing platform for executing multiple instructions. Optionally, the data processor includes a volatile memory for storing instructions and / or data and / or a non-volatile memory, such as a magnetic hard disk and / or a removable medium for storing instructions and / or data. Optionally, a network connection is also provided. A display and / or a user input device, such as a keyboard or a mouse, is also optionally provided.

[0041] Any combination of one or more computer-readable media may be used in some embodiments of the present invention. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or apparatus, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer-readable storage media would include the following: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device or apparatus.

[0042] A computer readable signal medium may include a propagated data signal containing a computer readable program code, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including but not limited to electromagnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0043] Program code contained on computer readable media and / or data used thereby may be transmitted using any appropriate media, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0044] Computer program code for performing operations of some embodiments of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer as a stand-alone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, via the Internet using an Internet service provider).

[0045] Some embodiments of the present invention are described below with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each frame of the flowchart and / or block diagram and the combination of frames in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to generate a machine, so that the instructions are executed via the processor of the computer or other programmable data processing device to create a device for implementing the functions / actions specified in the flowchart and / or block diagram blocks.

[0046] These computer program instructions may also be stored in a computer-readable medium, which may instruct a computer, other programmable data processing apparatus, or other device to act in a specific manner, so that the instructions stored in the computer-readable medium produce a product including instructions for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0047] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide a process for implementing the functions / actions specified in one or more block diagrams of the flowchart and / or block diagram.

[0048] Some of the methods described herein are generally designed only for use with computers and may not be feasible or practical for purely manual performance by a human expert. A human expert who would like to perform a similar task manually (e.g., determining the location of electrical leads in the brain based on recorded electrical signals) may be expected to use an entirely different approach, e.g., utilizing expert knowledge and / or the pattern recognition capabilities of the human brain, which is more efficient than manually completing the steps of the methods described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] By way of example only, some embodiments of the present invention are described herein with reference to the accompanying drawings. With specific reference now to the accompanying drawings in detail, it is emphasized that the details shown are by way of example and for the purpose of illustrative discussion of embodiments of the present invention. In this regard, the description in conjunction with the accompanying drawings makes it clear to those skilled in the art how to implement embodiments of the present invention.

[0050] In the attached picture:

[0051] Figure 1A is a flow chart of a general process for programming a brain stimulation system (e.g., a DBS system) according to some embodiments of the present invention;

[0052] Figure 1B is a flow chart of a detailed process for programming a brain stimulation system (e.g., a DBS system) according to some embodiments of the present invention;

[0053] Figure 1C is a flow chart of a general process for evaluating a current situation and future consideration points when selecting treatment parameter values ​​according to some embodiments of the present invention;

[0054] Figure 1D is a flow chart of a process for selecting a treatment parameter value according to some embodiments of the present invention;

[0055] Figure 1E is a schematic diagram of a treatment space according to some exemplary embodiments of the present invention;

[0056] Figure 2A is a block diagram of a system for assessing a patient's condition and selecting treatment parameter values ​​according to some embodiments of the present invention;

[0057] Figure 2Bis a schematic diagram of a treatment effect modifier and a processing method for optimizing certainty according to some embodiments of the present invention;

[0058] Figure 3 is a block diagram of a system for assessing a patient's condition according to some embodiments of the present invention;

[0059] Figure 4A is a flow chart of a general process for quantifying a patient's condition after task performance according to some embodiments of the present invention;

[0060] Figure 4B is a flow chart of a process for quantifying a patient condition using statistical inference and / or machine learning methods according to some embodiments of the present invention;

[0061] Figure 5 is a flow chart of a process for quantifying neurological disease symptoms and / or treatment side effects according to some embodiments of the present invention;

[0062] Fig. 6A is a flow chart of a process for pulse generator programming based on quantitative assessment of patient symptoms and treatment side effects according to some embodiments of the present invention;

[0063] Figure 6B is a flow chart of a process for programming a pulse generator based on a quantitative assessment of patient symptoms and treatment side effects and prior data from prior assessments (e.g., data from a large dataset and / or operating room electrophysiology) according to some embodiments of the present invention;

[0064] Fig. 7A is a flow chart of a process for generating at least one index according to some embodiments of the present invention;

[0065] Figure 7B is a flow chart of a process for generating at least one index after separating tremor and non-tremor related signals according to some embodiments of the present invention;

[0066] Figure 7C is a flow chart of a process for task-related index calculation compared to a baseline according to some embodiments of the present invention;

[0067] FIG. 8A to FIG. 8C is a flow chart of different methods for separating tremor-related signals from non-tremor-related signals according to some embodiments of the present invention;

[0068] 9A to 9C is a set of graphical panels illustrating the application of an absolute value, a flow chart of different processes used in experiments and in accordance with some embodiments of the present invention for identifying a tremor signal;

[0069] Fig.9D yes 9A to 9C A graphical representation of the results of a process described according to some embodiments of the invention;

[0070] FIG. 9E to FIG. 9G is a table showing the correlation between the analysis results and the manual evaluation in the experimental analysis;

[0071] Figure 9H shows a high pass filter with a 1 Hz cutoff used in experiments and according to some embodiments of the present invention;

[0072] FIG. 10A to FIG. 10B A schematic diagram showing multiple locations for placing multiple EMG electrodes used in experiments and according to some embodiments of the present invention;

[0073] FIG. 11A to FIG. 11F and Fig.12 Graphs showing different analysis stages of signals received from EMG electrodes as used in experiments and according to some embodiments of the present invention;

[0074] FIG. 13A to FIG. 13B is a panel showing graphs of results of a tremor analysis process and a rigidification analysis process performed during an experiment according to some embodiments of the present invention;

[0075] Fig.14A is a schematic diagram of multiple locations on a face for placing electrodes for gaze assessment according to some embodiments of the present invention;

[0076] FIG. 14B to FIG. 14I a schematic diagram showing the stages and results of a gaze analysis process performed during an experiment and according to some embodiments of the present invention;

[0077] Fig.15A is a schematic diagram showing multiple locations on a face of an electrode used in an experiment for placing electrodes for evaluating internal capsule recruitment according to some embodiments of the present invention;

[0078] FIG. 15B to FIG. 15C A schematic diagram showing identification of a signal segment indicating an action motion in experiments and according to some embodiments of the present invention; and

[0079] FIG. 16A to FIG. 16D are multiple screen shots of a display of a software for assessing a patient's condition according to some embodiments of the present invention. DETAILED DESCRIPTION

[0080] The present invention, in some embodiments thereof, relates to treatment space assessment, and more particularly, but not limited to, treatment space assessment for a brain stimulation therapy.

[0081] An object of some embodiments relates to programming a brain stimulation system, such as a DBS system, based on a quantitative assessment of at least one side effect of the treatment and / or at least one symptomatic effect of the treatment. In some embodiments, the quantitative assessment of at least one side effect and / or at least one symptomatic effect of the treatment is used to update unfinished programming performed during an implantation procedure (e.g., a procedure in an operating room) of at least one stimulation electrode or electrode lead. In some embodiments, the quantitative assessment of at least one side effect of the treatment and / or at least one symptomatic effect of the treatment is used to update a therapeutic space map defined during the procedure. In some embodiments, the programming is performed outside of the operating room.

[0082] According to some embodiments, an assessment system (e.g., a patient condition assessment system) is used to quantitatively assess at least one side effect and / or at least one symptomatic effect. In some embodiments, the system provides a feedback to a person (e.g., a programmer) programming the DBS system regarding one or more sets of treatment parameter values. In some embodiments, the feedback is generated based on the assessment of at least one side effect and / or at least one symptomatic effect and is communicated to the programmer. In some embodiments, the feedback is generated and provides options for programming the DBS system using a set of treatment parameter values ​​selected by the programmer. In some embodiments, the treatment parameters include stimulation location, number of stimulation electrodes, location of stimulation electrodes, combination of stimulation electrodes, stimulation amplitude, stimulation frequency, stimulation pulse width, and stimulation duration.

[0083] According to some embodiments, the assessment system generates and provides the feedback to the programmer based on the quantitative assessment performed and / or information manually inserted into the system by a user of the system or a human programmer. Alternatively or additionally, the assessment system generates and communicates the feedback to the programmer based on information from a large data set collected from multiple patients.

[0084] An object of some embodiments relates to selecting treatment parameter values ​​for a neurological treatment, such as a brain stimulation treatment, based on an expected future flexibility of the treatment, such as an expected buffer space. In some embodiments, treatment parameter values ​​are selected based on an expected future flexibility of a particular set of treatment parameter values, such as when delivering stimulation at a selected location within the brain. In some embodiments, when selecting treatment parameter values, the expected future flexibility is quantified and the quantified results are used. In some embodiments, the quantified results indicate the level of flexibility required to allow future modification of the treatment when a particular set of treatment parameter values ​​is selected. In some embodiments, treatment parameter values ​​are selected based on the expected future flexibility and a quantitative assessment of the patient's condition, such as a quantitative assessment of at least one side effect of the therapy and / or at least a symptomatic effect.

[0085] According to some embodiments, treatment parameter values ​​are selected during an implantation procedure of at least one stimulation electrode or electrode lead. In some embodiments, the selected treatment parameter values ​​are used to program a stimulation system, such as a DBS system in an operating room. In some embodiments, feedback is communicated to a programmer of the DBS system, such as a surgeon, regarding the potential of the treatment parameter values ​​selected by the programmer for programming. In some embodiments, feedback is generated and communicated to the programmer based on a comparison between the future flexibility of the selected treatment parameter values ​​and the expected future flexibility. In some embodiments, the feedback includes recommendations for one or more alternative treatment parameter value combinations. In some embodiments, during the implantation procedure in the operating room, at least one stimulation electrode or electrode lead is moved to a different position based on the communicated feedback. In some embodiments, the feedback is used.

[0086] According to some embodiments, the expected future flexibility is based on an estimated change in the future of at least one therapeutic effect modifier that can affect the delivered therapy. Alternatively or additionally, the expected future flexibility is based on an optimization certainty that at least one stimulation electrode or electrode lead is located at a desired location within the brain, or based on a certainty that an optimization process for selecting therapeutic parameter values ​​is completed within a predetermined time period, such as during an implantation procedure. In some embodiments, the expected future flexibility is estimated at least one day, at least one week, at least one month, at least one year, at least 10 years, or any intermediate, shorter, or longer time period after an implantation procedure of at least one electrode or an electrode lead in a patient's brain, and in various embodiments, after programming a pulse generator, such as an implantable pulse generator (IPG).

[0087] According to some embodiments, the expected future flexibility is quantified based on measurements of an assessment system that measures a single patient's response and / or condition to delivered stimulation using at least one combination of treatment parameter values. Alternatively, the expected future flexibility is quantified based on a large data set. In some embodiments, the large data set is generated by collecting data from multiple patients, the data containing information about the effect of one or more treatment effect modifiers on stimulation therapy at different time periods after implant surgery and / or after an IPG reprogramming. In some embodiments, one or more of at least one algorithm, at least one statistical method, at least one lookup table is applied to the large data set to generate a numerical value, such as a score, for the potential effect of one or more treatment effect modifiers on multiple outcomes of a stimulation therapy. In some embodiments, the numerical value is generated for the potential effect of one or more treatment effect modifiers on a treatment delivered using one or more of a set of specific treatment parameter values, a specific stimulation location, a specific number and / or combination of stimulation electrodes delivering stimulation. In some embodiments, a user manually inserts information related to expected future flexibility into an assessment device.

[0088] According to some embodiments, a plurality of patient measured features are matched with previous data. In some embodiments, the matching is used to predict the patient's behavior. In some embodiments, the prediction is based on a plurality of patients with similar anatomy, disease progression, and / or stimulation devices.

[0089] According to some exemplary embodiments, a quantification of the expected future flexibility is presented to a user, such as an expert. In some embodiments, the quantification is presented, for example, on a screen, relative to one or more of a set of specific treatment parameter values, such as relative to a selected combination of a value of a first treatment parameter (e.g., stimulation amplitude) and a value of a second treatment parameter (e.g., frequency). In some embodiments, the plurality of additional treatment parameters includes stimulation duration, number of stimulation pulses, stimulation location, location of at least one electrode used for stimulation, number of electrodes used for stimulation, and a specific combination of electrodes used for stimulation.

[0090] According to some embodiments, the display to the user includes two or more sets of treatment parameter values, two or more sets of side effects and / or symptomatic effects and / or the treatment space, such as the shape and / or size of the treatment space.

[0091] According to some embodiments, information received from the evaluation system, such as quantification of the patient's condition, mapping of the treatment space, and / or calculation of a desired future flexibility, is used to determine whether an electrode or electrode lead is positioned at a desired stimulation location and / or a selected electrode configuration is a desired configuration.

[0092] According to some exemplary embodiments, two or more stimuli are delivered to the brain, each stimulus having a different combination of stimulation parameter values. In some embodiments, each stimulation parameter value combination calculates an expected future flexibility value, such as a score. In some embodiments, the future flexibility value is a range of multiple values ​​in at least one treatment parameter, such as a range of stimulation intensity, a range of stimulation frequency values. In some embodiments, the score represents the level of flexibility required to modify the treatment method in the future when a combination is selected from at least two different stimulation parameter value combinations or from at least one estimated value combination for stimulation to obtain a different potential treatment parameter value combination. In some embodiments, the calculated score is used, for example, to rank the potential treatment parameter value combinations. In some embodiments, the ranking and calculated score for each treatment parameter value combination are presented to the user, for example, on a display.

[0093] According to some embodiments, the at least one therapeutic effect modifier comprises an estimated change in current disease symptoms and / or future disease symptoms. Alternatively or additionally, at least one therapeutic effect modifier comprises an estimated change in the patient's current medication regimen and / or the patient's future medication regimen, for example due to future age or clinical condition. Alternatively or additionally, at least one therapeutic effect modifier comprises a healing process from the implantation procedure. In some embodiments, during the healing process, changes in the tissue surrounding the stimulation electrodes or changes in the tissue in contact with the stimulation electrodes can change the tissue's response to the delivered therapy, change the effect of the therapy on disease symptoms and / or change the appearance of side effects.

[0094] According to some embodiments, at least one therapeutic effect modifier includes a stimulation location, for example, estimating changes in the stimulation location in the future, using different stimulation electrodes or different combinations of stimulation electrodes. Optionally or additionally, the at least one therapeutic effect modifier includes disease progression, such as progression of a disease or a specific type of disease in the future. In some embodiments, progression of a disease optionally leads to the need to provide more robust treatment, for example by changing a treatment parameter value. Alternatively or additionally, the at least one therapeutic effect modifier includes multiple stimulation parameter values, such as estimated changes in future stimulation parameter values. Optionally or additionally, the at least one therapeutic effect modifier includes treatment side effects, such as estimated changes in future treatment side effects. Optionally, the treatment side effects are side effects of a combination between one or more drugs administered to the patient and the stimulation therapy.

[0095] According to some embodiments, a future flexibility level, such as a value or a score, is updated, for example, when stimulation electrodes are implanted in the patient's brain and / or therapy is delivered. In some embodiments, the future flexibility level is updated based on a measurement of the patient's condition, such as a measurement of at least one symptomatic effect and / or at least one treatment side effect performed while the patient is at home or in a clinic. In some embodiments, the future flexibility level is updated based on a change in at least one treatment effect modifier or a change in a score of the at least one treatment effect modifier. In some embodiments, the future flexibility level is updated based on information received from an analysis of a large data set.

[0096] According to some exemplary embodiments, for example, if the updated future flexibility level is not the desired future flexibility level, an indication, such as an alarm signal, is transmitted to the patient and / or a specialist or person monitoring the patient's condition. In some embodiments, the patient and / or specialist stops the stimulation treatment and / or programs the stimulation system with a different set of treatment parameter values. In some embodiments, if the updated future flexibility level is less than a predetermined value, an alarm signal is transmitted. In some embodiments, the indication or updated future flexibility level is transmitted to the specialist or person monitoring the patient's condition by wireless transmission or other telemedicine methods.

[0097] An object of some embodiments relates to mapping a treatment space, such as a therapeutic window (TW) of a stimulation therapy, e.g., a brain stimulation therapy, based on a desired future flexibility of the therapy. In some embodiments, the defined treatment space includes at least one set of treatment parameter values ​​that result in a desired therapeutic effect on a patient, and has a desired future flexibility that allows for multiple treatment parameter values ​​to be changed in the future while maintaining the desired therapeutic effect, and optionally maintaining a desired level of side effects.

[0098] According to some embodiments, the treatment space is mapped based on a quantitative assessment of treatment side effects and symptomatic effects during and / or after stimulation and / or based on a quantification of expected future flexibility. In some embodiments, the term stimulation period as used refers to a process in which one or more stimulation pulses are effectively delivered to tissue. In some embodiments, the term "during stimulation" refers to during at least one stimulation period. In some embodiments, the term "after stimulation" refers to after at least one stimulation period when stimulation is not actively delivered to tissue. In some embodiments, the treatment space is defined according to a specific stimulation position and / or according to a specific combination of two or more stimulation electrodes used to deliver the stimulation treatment. In some embodiments, the treatment space is mapped according to a fixed position of at least one stimulation electrode or electrode lead, taking into account that the electrodes or electrode leads cannot be moved after surgery.

[0099] According to some embodiments, the treatment space is displayed to a user, for example, by a graphical representation of the treatment space. In some embodiments, the treatment space includes two or more areas that differ based on symptom effect level, side effect level and / or future flexibility. In some embodiments, two or more areas are generated by clustering combinations of treatment parameter values ​​that produce symptom effects and / or cause side effects within a predetermined range of multiple values. In some embodiments, two or more areas are generated by clustering multiple treatment parameter values ​​for a future flexibility level within a predetermined range of multiple values. In some embodiments, two or more areas are scored, for example, based on the level of one or more of the symptom effect level, side effect level, or future flexibility level. In some embodiments, the graphical representation of the treatment space includes graphical representations of two or more areas and / or scores for the groups.

[0100] According to some embodiments, the treatment space is updated based on future changes to one or more treatment effect modifiers. Alternatively or additionally, the treatment space is updated based on a future assessment of the patient's condition, such as an assessment of symptom effects and / or treatment side effects. In some embodiments, the updated treatment space is stored in a memory of an assessment device. In some embodiments, if the updated treatment space is not the desired treatment space, an indication (e.g., an alarm signal) is transmitted to the patient or a person monitoring the patient's condition, for example, by wireless transmission or other telemedicine methods. In some embodiments, an alarm signal is transmitted if the updated treatment space size decreases below a predetermined value.

[0101] According to some embodiments, the evaluation system and method described herein are used in an operating room. In some embodiments, in the operating room, a programmer (e.g., a user of the system) determines which stimulation to perform and where in the brain. In some embodiments, the evaluation system provides a mapping of the treatment space to the programmer, for example in the form of a graphical representation of the treatment space. In some embodiments, the programmer uses the mapping to verify a set of selected treatment parameter values. Alternatively or additionally, the programmer selects at least one set of alternative parameter values ​​based on the information in the mapping, for example, a set of treatment parameter values ​​included in the mapping. In some embodiments, the evaluation system and evaluation method are used in an operating room to ensure that an implanted stimulation electrode or electrode lead is placed in a correct position before completing a surgical procedure. In some embodiments, stimulation is performed from a sensitive navigation electrode or from one or more contacts of an implanted electrode lead.

[0102] According to some embodiments, the evaluation systems and methods described herein are used in an IPG programming session, for example, a programming session performed outside an operating room, for example, in a clinic. In some embodiments, in the programming session, the evaluation results are used to select a set of optimal treatment parameters for chronic treatment.

[0103] According to some embodiments, during the programming session, the evaluation system provides the user with suggested combinations of treatment parameter values, or parameters that will result in the most effective search for DBS parameters that are most likely to end satisfactorily in the shortest amount of time.

[0104] One aspect of some embodiments relates to the use of an electrooculogram (EOG) to quantify stimulation-induced gaze disturbance side effects. In some embodiments, the effect of brain stimulation on gaze caused by stimulation is quantified by comparing signals associated with eye movements recorded before stimulation with signals associated with eye movements recorded during and / or after stimulation. In some embodiments, multiple segments indicative of eye movements in the multiple signals recorded are identified. In some embodiments, a numerical value associated with the change in the signal in the segment is calculated. In some embodiments, stimulation-induced gaze disturbance is identified and quantified by comparing multiple numerical values ​​associated with the calculated change between the signal recorded before stimulation and the signal recorded during and / or after stimulation.

[0105] An object of some embodiments relates to quantifying rigidity based on signals measured before and after brain stimulation. In some embodiments, the signal is measured by at least one electrode, such as an EMG electrode connected to the patient. In some embodiments, rigidity is quantitatively assessed in the operating room, such as during implantation surgery. In some embodiments, rigidity is quantified by measuring average signal characteristics around at least one selected time point in the signal. Alternatively or additionally, rigidity is quantified by calculating at least one central tendency parameter of at least one calculated average signal. In some embodiments, rigidity is quantified by identifying a reduction in power in a frequency band between 20 and 2000 Hz, such as 20 to 500 Hz, 200 to 1000 Hz, 1000 to 2000 Hz, 1500 to 2000 Hz, or any intermediate, smaller, or larger range in the signal induced by stimulation, such as a signal recoded during a stimulation period.

[0106] A potential advantage of receiving feedback from an evaluation system as described herein during the implantation process in the operating room is that at least one stimulation electrode or electrode lead can be moved to a different location within the brain. A potential advantage of receiving feedback from an evaluation system outside the operating room is that there is more time to fine-tune the treatment parameter values, for example to achieve optimal treatment effect.

[0107] According to some embodiments, the methods and systems described below are used outside of an operating room, such as in a clinic.

[0108] Before explaining at least one embodiment of the present invention in detail, it should be understood that the present invention is not necessarily limited in its application to the details of the construction and arrangement of the components and / or methods described in the following description and / or illustrated in the drawings and / or embodiments. The present invention can have other embodiments or can be implemented or carried out in various ways.

[0109] Exemplary General Programming Process

[0110] According to some exemplary embodiments, the evaluation systems and methods described herein are used to program a brain stimulation system, such as a pulse generator of a brain stimulation system outside of an operating room. In some embodiments, the programming is performed after the healing process from the implantation surgery. Figure 1A , Figure 1A A general programming process following the implantation process according to some exemplary embodiments of the present invention is depicted.

[0111] According to some exemplary embodiments, a stimulation system (e.g., at least one stimulation electrode or electrode lead) is implanted into the brain of a patient at block 101. In some embodiments, the stimulation system is implanted in an operating room during an implantation surgery. In some embodiments, the stimulation system is programmed with an unfinished program in the operating room.

[0112] According to some exemplary embodiments, a stimulation system, such as at least one stimulation electrode or electrode lead, is implanted into the brain of a patient at block 101. In some embodiments, the stimulation system is implanted in an operating room during an implantation procedure. In some embodiments, the stimulation system is programmed with an unfinished program in an operating room.

[0113] According to some exemplary embodiments, at block 103 , the patient leaves the operating room.

[0114] According to some exemplary embodiments, the patient's condition is assessed at block 105. For example, at least one treatment side effect and / or at least one symptomatic effect. In some embodiments, the patient's condition is assessed during a programmed session, such as a programmed session performed at the patient's home or in a clinic. In some embodiments, the patient's condition is assessed during or after a recovery period from an implantation procedure.

[0115] According to some exemplary embodiments, the stimulation system, such as a pulse generator of the stimulation system, is programmed at box 107. In some embodiments, the stimulation system is programmed during a programming session and performed at the patient's home or clinic. In some embodiments, the stimulation system is programmed based on the results of an assessment of the patient's condition. Additionally, the stimulation system is programmed based on desired future flexibility. In some embodiments, the stimulation system is programmed based on information received from a programmer performing the programming and / or information received from a remote computer or remote server. In some embodiments, the programming includes updating the unfinished procedure in the operating room.

[0116] Exemplary detailed programming process

[0117] Reference now Figure 1B , Figure 1B Detailed programming processes according to some exemplary embodiments of the present invention are depicted.

[0118] According to some exemplary embodiments, for example Figure 1A As described above, at box 105 the patient's condition is assessed.

[0119] According to some exemplary embodiments, a treatment space is mapped at block 109. Alternatively, an existing treatment space is updated at block 109, such as a treatment space mapped during an implantation procedure. In some embodiments, the treatment space is mapped or updated based on an assessment of the patient's condition. Additionally, the treatment space is mapped or updated based on desired future flexibility.

[0120] According to some exemplary embodiments, at least one selectable combination of treatment parameter values ​​is provided to the evaluation system at block 111. In some embodiments, the at least one selectable combination of treatment parameter values ​​is provided by a user, such as a programmer, during a programming session.

[0121] According to some exemplary embodiments, a relationship between the provided combination and the treatment space is determined at block 113. In some embodiments, the evaluation system determines whether the provided combination is included in the treatment space. In some embodiments, the evaluation system determines a distance between the provided combination and an edge of the treatment space.

[0122] According to some exemplary embodiments, an indication of the determined relationship is transmitted at block 115. In some embodiments, the evaluation system transmits the indication to a user (e.g., a programmer). In some embodiments, the evaluation system provides the user with an indication of the ability to program the stimulation system using the provided combination as feedback.

[0123] According to some exemplary embodiments, multiple alternative treatment parameter value combinations are suggested at box 117. In some embodiments, the evaluation system suggests multiple alternative combinations to the user, such as the provided treatment parameter value combinations, for example based on input from the user. In some embodiments, the evaluation system suggests multiple alternative combinations based on the treatment space. In addition, the evaluation system proposes multiple alternative combinations based on a desired future flexibility. In some embodiments, the system suggests multiple alternative combinations based on information received from the user, from the patient, and / or from a large data set collected from multiple patients.

[0124] According to some exemplary embodiments, a user, such as a human programmer, selects a set of treatment parameter values, e.g., from a list of suggested multiple combinations, for programming the stimulation system at block 107. In some embodiments, the user selects a combination for programming based on instructions communicated at block 115. In some embodiments, the user selects a combination based on information displayed by the system regarding treatment space and / or desired future flexibility.

[0125] Exemplary General Procedure for Quantifying Expected Moderators of Treatment Effects

[0126] According to some exemplary embodiments, machine learning / statistical methods are used to quantify treatment effect modifiers, e.g. Figure 4B In some embodiments, in the IPG programming and in the home system version, data marked by an expert (neurologist is evaluating the patient over time) is combined with the assessment of the patient's condition over time. In some embodiments, the received data is marked by the expert or the system. In some embodiments, in e.g. Figure 4B The pre-operational and intra-operational data are used in one of the methods described in (e.g., as described below) to obtain the most accurate prediction of the change in the post-operational measurements.

[0127] According to some exemplary embodiments, the treatment effect modifier is quantified based on a large data set collected from multiple patients. In some embodiments, the large data set is generated by collecting data prior to surgery, for example, data related to disease stage and duration, severity of symptoms using the assessment system described herein or clinical assessment, severity of drug side effects using the system described herein or clinical assessment, medication regimen history, family disease, genetic indications, imaging data and / or mobile phone data or data collected by different sensors. One or more data related to data, such as GPS data, which is optionally related to how much the patient walks; accelerometer data, optionally related to the patient's small-scale movements; and / or microphone, which is optionally related to clarity quality.

[0128] According to some exemplary embodiments, data is collected during a DBS procedure, such as one or more of general medical and demographic data, MER data, stimulation quantification data, video data, audio data, and decision-related data, such as deciding where to implant stimulation electrodes or leads.

[0129] According to some exemplary embodiments, data is collected after the implantation surgery, such as updated information about the treatment space using different combinations of treatment parameter values, data from patients in their home environment who at least occasionally use the assessment system to assess their symptoms / side effects. In some embodiments, the system includes an input device such as a tablet computer, where the patient performs additional tasks, enters their personal self-assessment, or plays games and / or participates in other interactive activities, such as activities that include providing input to the device and can also quantify their movement status. In some embodiments, the data includes mobile phone data or data received from other sensors, such as GPS data, accelerometer data, data from medical records, data collected from neurologist visits, data related to medication changes, and / or imaging data.

[0130] According to some exemplary embodiments, preoperative and intraoperative data are used to predict how to improve treatment in the future. In some embodiments, a large data set infrastructure is used, which can store many megabytes or even possible megabytes of data, and computational algorithms can be applied to large data sets, such as unlabeled data to extract information. In some embodiments, the algorithms applied include algorithms that extract the most important information from medical records, such as J. Jiang "Extracting Information from Text", CC Aggarwal, C. Zhai (Eds.), Journal of Mining Text Data, Springer, USA (2012), pages 11 to 41. Audio analysis techniques are also included to extract information indicating the patient's condition. Some of these may depend on how we quantify dysarthria in our system, some of which may be as described in J. Hirschberg, A. Hjalmarsson, N. Elhadad "Your Disease Is as Serious as Your Voice": Modeling Speaker State Using Computational Methods to Assess Disease and Recovery, A. Neustein (Editor), "Advances in Speech Recognition", Springer, USA (2010), pages 305 to 322.

[0131] In some embodiments, processing the data includes extracting meaningful information from a video of the patient, such as information recorded by an evaluation system before, during, or after surgery, optionally in combination with one or more indexing techniques, such as those described in Wenhua Hu, Guoen Xie, Lei Li, Guoqiang Zeng, and S. Maybank, "A Survey on Video Indexing and Retrieval Based on Visual Content," IEEE Journal of Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(6) (2011), pp. 797-819.

[0132] According to some exemplary embodiments, one or more methods are used to perform a method of combining information extracted from various sources and using the information to find a relationship between a patient's condition before and during surgery and how that condition will change in the future. This is described in J. Fan, F. Han, "Challenges in Big Data Analysis," National Science Review, Vol. 1(2), 2014, pp. 293-314, or J. Fan, J. Lv, "Deterministic Independence Screening in Ultra-High Dimensional Feature Spaces," Journal of the Royal Statistical Society: Series B (Statistical Methods), 70(5) (2008), pp. 849-911.

[0133] Exemplary General Process for Selecting Treatment Parameters

[0134] According to some exemplary embodiments, parameter values ​​for stimulation therapy, such as DBS therapy, are selected based on the current state of the disease, system, and patient, as well as the patient's future needs for therapy. Figure 1C , Figure 1C A general process for selecting stimulation parameters according to some exemplary embodiments of the present invention is depicted.

[0135] According to some exemplary embodiments, at least one stimulating electrode is located within the brain. In some embodiments, the at least one stimulating electrode is located on an electrode lead, which is inserted into the brain, such as an electrode lead in the shape of a needle. In some embodiments, at least one stimulating electrode is part of a plurality of electrodes that are axially and / or circumferentially displaced on an outer surface of the lead. In some embodiments, at least one stimulating electrode is placed in contact with brain tissue. In some embodiments, at least one stimulating electrode and / or electrode lead is positioned at a predetermined location within the brain, such as at a desired anatomical and / or functional location.

[0136] According to some exemplary embodiments, at block 102, at least one stimulation electrode is placed within the brain. In some embodiments, during implantation, at least one stimulation electrode is placed within the brain. In some embodiments, at least one stimulation electrode is located on an electrode lead placed into the brain, such as an electrode lead in the shape of a needle.

[0137] According to some exemplary embodiments, a plurality of initial stimulation parameter values ​​are selected at box 104. In some embodiments, a plurality of initial stimulation parameter values ​​are selected based on the location of the stimulation electrodes within the brain. Additionally or alternatively, the stimulation parameter values ​​are selected based on safety considerations. Optionally, the initial stimulation parameter values ​​are selected based on knowledge from a large data set including data collected from multiple patients. In some embodiments, the stimulation parameters include stimulation amplitude, stimulation frequency, stimulation duration, the number of stimulation pulses in a pulse train, the duration of each individual pulse, or pulse width, number of pulse trains, total number of stimulation pulses in a time period, such as one or more of per minute, per hour, per day.

[0138] According to some exemplary embodiments, stimulation is delivered via at least one stimulation electrode at block 106. In some embodiments, stimulation is delivered according to the selected plurality of initial stimulation parameter values.

[0139] According to some exemplary embodiments, a quantitative assessment of treatment side effects is performed at box 110. In some embodiments, treatment side effects include gaze deviation and diplopia, unclear speech clarity (dysarthria), persistent activation (dilation) of leg, arm or facial muscles, and involuntary movements (dyskinesia). In some embodiments, the quantitative assessment is performed in a timed relationship with the delivery of the stimulation, such as during and / or after the delivery of the stimulation. In some embodiments, the quantitative assessment is performed over a time period of up to 30 minutes, such as a maximum of 10 minutes, a maximum of 5 minutes, a maximum of 1 minute, a maximum of 30 seconds, or any intermediate, shorter or longer time period from the end of the stimulation. Alternatively or additionally, the quantitative assessment is performed at least 1 second from the start of the stimulation, such as 1 second, 10 seconds, 30 seconds, 1 minute, 10 minutes, or any intermediate, shorter or longer time period from the start of the stimulation.

[0140] According to some exemplary embodiments, a quantitative assessment of disease symptoms is performed at box 112. In some embodiments, disease symptoms include muscle stiffness (resistance to passive movement of a limb), tremor, and bradykinesia, which is defined as slow movement or lack of movement. In some embodiments, the quantitative assessment is performed in a timed relationship with the delivery of the stimulation, such as before, during, and / or after the delivery of the stimulation. In some embodiments, the quantitative assessment is performed within a time period of up to 30 minutes, such as a time period of up to 10 minutes, up to 5 minutes, up to 1 minute, up to 30 seconds, or in any intermediate, shorter, or longer time period from the end of the stimulation. Alternatively or in addition, the quantitative assessment is performed at least 1 second from the start of the stimulation, such as 1 second, 10 seconds, 30 seconds, 1 minute, 10 minutes, or any intermediate, shorter, or longer time period from the start of the stimulation.

[0141] According to some exemplary embodiments, future considerations related to the stimulation therapy are evaluated, such as calculating an expected future flexibility of the therapy at block 114. In some embodiments, the expected future flexibility is based on an estimated change in the future of at least one therapeutic effect modifier that can affect the delivered therapy. In some embodiments, at least one therapeutic effect modifier includes a healing process, such as a healing process of tissue surrounding at least one stimulation probe, disease progression, drug state that varies over time, a potential need to change the stimulation location, disease symptoms and treatment side effect timing that vary over time, and / or stimulation parameters that may need to change over time. In some embodiments, the expected future flexibility is calculated with sufficient future flexibility to allow programming to be adjusted after the implantation procedure, such as after the patient recovers from the operating room or during recovery from surgery.

[0142] According to some exemplary embodiments, the overall information provided to the user or system is evaluated at block 116. In some embodiments, if the information provided is insufficient to allow selection of treatment parameter values, new stimulation parameters are selected at block 118 instead of the initial treatment parameter values, and the evaluation process is repeated by delivering stimulation at block 106 using the new stimulation parameter values. Optionally, at least one stimulation electrode or electrode lead is moved to a different location.

[0143] According to some exemplary embodiments, if the provided information is sufficient, the information is ranked at block 120. In some embodiments, the information is ranked using one or more statistical methods and / or algorithms (e.g., machine learning algorithms). In some embodiments, the information is ranked, for example, to generate one or more recommendations to a user of the device, for example, to an expert.

[0144] According to some exemplary embodiments, at block 122, an indication is communicated to the expert. In some embodiments, the indication is a human-detectable indication, optionally provided on a display. In some embodiments, the indication is a graphical indication showing a ranking of one or more options according to the selected evaluation system.

[0145] Exemplary Detailed Process for Selecting Treatment Parameters

[0146] Reference now Figure 1D , Figure 1D A detailed process of selecting treatment parameters based on current status and future considerations according to some exemplary embodiments of the present invention is depicted.

[0147] According to some exemplary embodiments, a stimulus is delivered at block 106, e.g. Figure 1C According to some exemplary embodiments, at block 132, disease symptoms and / or treatment side effects are quantified, e.g. Figure 1C described.

[0148] According to some exemplary embodiments, the stimulation is repeated at block 134. In some embodiments, the stimulation is repeated at least once, for example 2, 3, 5, 10 times or any intermediate, smaller or larger number of times. In some embodiments, the stimulation is repeated each time with a different treatment parameter value.

[0149] According to some exemplary embodiments, a treatment space is defined at block 136. In some embodiments, a treatment space, such as a multi-dimensional space, is defined by two or more treatment parameter values ​​that promote a treatment effect. In some embodiments, the treatment space is defined based on treatment parameter values ​​used for stimulation and quantifying disease symptoms and treatment side effects after or during stimulation.

[0150] According to some exemplary embodiments, information about treatment effect modifiers that can change the treatment effect on the patient in the future is provided at box 138. In some embodiments, the treatment effect modifiers include disease symptoms that change over time, drug status that changes over time, healing process, possible changes in stimulation location, disease progression, stimulation parameter values ​​that change over time, and treatment side effects that change over time. In some embodiments, the information is provided as statistical information, such as an index or score, which indicates the likelihood that a particular treatment effect modifier will affect the treatment effect in the future based on the selected stimulation parameter value.

[0151] According to some exemplary embodiments, a desired future flexibility, such as a desired modification range, is calculated at block 140. In some embodiments, the desired modification range is based on information about the treatment effect modifier, the treatment space, and the selected treatment parameter value. In some embodiments, the desired modification range is a calculated or estimated range within which the selected treatment parameter value must be changed based on the effect of the treatment effect modifier in order to keep the provided stimulation therapy within the defined treatment effect.

[0152] According to some exemplary embodiments, an optimization certainty is provided at block 142. In some embodiments, an optimization certainty refers to the degree of certainty that an optimization process for a treatment parameter value is completed when the patient is in the operating room, starting from a selected treatment parameter value represented by a point in the treatment space, and based on the desired range of modifications, the treatment space, and the disease modifier. In some embodiments, an optimization certainty is calculated or estimated based on the desired range of modifications, the treatment space for the selected treatment parameter value.

[0153] According to some exemplary embodiments, treatment parameter values ​​are selected at block 144. In some embodiments, at least one set (e.g., at least 2, 4, 10 sets) or any intermediate, larger, or smaller number of combinations of treatment parameter values ​​are selected at block 144. In some embodiments, a set of treatment parameter values ​​includes values ​​for different treatment parameters. In some embodiments, the selected treatment parameter values ​​are selected based on a currently defined treatment space, a calculated expected range of modifications involving future events, and optionally based on optimization certainty.

[0154] According to some exemplary embodiments, at block 146, an implanted pulse generator (IPG) is automatically reprogrammed using at least one set of selected therapy parameter values. In some embodiments, once programming is complete, therapy is delivered at block 148.

[0155] Optionally, and according to some exemplary embodiments, an indication, such as a human detectable indication, is communicated to the user at block 150. In some embodiments, the indication is a graphical representation. In some embodiments, the indication includes information about the selected at least one set of treatment parameter values.

[0156] According to some exemplary embodiments, for example, if the selected treatment parameter value is not the desired treatment parameter value, the user moves at least one stimulation electrode to a different location within the brain at block 152. In some embodiments, the user moves an electrode lead having at least one stimulation electrode disposed thereon to a different location within the brain.

[0157] According to some exemplary embodiments, a user manually programs the IPG based on selected therapy parameter values.

[0158] Exemplary Treatment Spaces

[0159] Reference now Figure 1E , which depicts a treatment space according to some exemplary embodiments of the present invention.

[0160] According to some exemplary embodiments, a multi-dimensional space 159 is Figure 1EThe two or more stimulation treatment parameters shown are defined in a coordinate system, such as stimulation parameter 1, such as stimulation amplitude, and stimulation parameter 2, such as stimulation frequency. In some embodiments, each point in the space represents a different set of treatment parameter values ​​in the two or more treatment parameters that constitute the coordinate system. In some embodiments, a treatment space, such as treatment space 160, is a space included in space 159, wherein the combination of treatment parameter values ​​included in the treatment space results in a treatment effect, such as a desired treatment effect. In some embodiments, the treatment space is personalized for a patient or a group of patients. In some embodiments, for example, a treatment space is generated by providing two or more stimulations using different combinations of treatment parameter values ​​and evaluating disease symptoms and side effects during or after each stimulation event. Alternatively or additionally, a treatment space is generated, for example, by providing two or more stimulations using different stimulation electrodes or different combinations of multiple stimulation electrodes.

[0161] According to some exemplary embodiments, treatment space 160 includes one or more regions in which stimulation using selected treatment parameters results in different levels of side effects, for example, region 162 includes stimulation parameter values ​​that result in a desired treatment effect with a high level of side effects, and region 164 includes stimulation parameter values ​​that result in a desired treatment effect with a low level of side effects.

[0162] According to some exemplary embodiments, the one or more regions include combinations of treatment parameter values ​​clustered based on similarity of treatment effect level, side effect level, or calculated future similarity level. In some embodiments, the similarity is based on a predetermined range or a predetermined threshold.

[0163] According to some exemplary embodiments, the treatment space, such as the size and / or shape of the treatment space, is determined based on quantification of at least one symptomatic effect, at least one side effect, and desired future flexibility. In some embodiments, the treatment space, such as the size and / or shape of the treatment space, can be updated as long as the patient continues to receive stimulation therapy. In some embodiments, the treatment space is updated based on measurements of at least one side effect and / or at least one symptom after the implantation procedure, after programming the IPG, such as when the patient is at home or in a clinic. In some embodiments, at least one indication of the updated treatment space, such as an alarm signal, is transmitted to the patient or a person monitoring the patient's condition.

[0164] According to some exemplary embodiments, if the updated treatment space, such as the size and / or shape of the treatment space is not the desired treatment space, an alarm signal is transmitted to the patient or a person monitoring the patient's condition. In some embodiments, the alarm signal is sent to a remote device, such as a remote computer or mobile device.

[0165] Exemplary system for assessing patient condition and selecting treatment parameters

[0166] According to some exemplary embodiments, a system for assessing a patient's condition and selecting treatment parameters is used to assess a patient's condition before, during, and / or after a brain stimulation treatment, such as a DBS treatment. In some embodiments, the assessment system communicates directly with the DBS system, such as with an implanted pulse generator (IPG) of the DBS system, to, for example, automatically modify the DBS treatment or its parameter values. Alternatively or additionally, the assessment system communicates with a subject receiving the DBS treatment and / or with an expert, such as a physician, technician, or nurse. In some embodiments, the subject and / or the expert modifies the DBS treatment or its parameter values ​​based on instructions received from the assessment system. Now referring to Figure 2A , Figure 2A Systems for assessing a condition of a subject according to some exemplary embodiments of the present invention are depicted.

[0167] According to some exemplary embodiments, a system for assessing a subject's condition includes an assessment device, such as device 204, and one or more sensors connected to device 204. In some embodiments, device 204 is a portable assessment device that is shaped and sized to be connected to the body of a subject being treated, such as to the subject's clothing, by at least one clip, hook, strap, or any other connector. In some embodiments, the weight of the assessment device is in the range of 100 to 500 grams. In some embodiments, the assessment device includes a laptop, a tablet computer, or a mobile device. Alternatively, the assessment device is a desktop device that is shaped and sized to be positioned on a table or a movable cart.

[0168] According to some exemplary embodiments, the system is composed of one or more lightweight (up to 100g) sensor modules attached to the patient's body. In some embodiments, the sensor module transmits wireless signals to the external module. In some embodiments, the external module is not attached to the patient's body and can be selectively located near the patient, such as in the patient's house, car, backpack or other portable bag. In some embodiments, the required signal processing, analysis and subsequent communication occur in the external module. In some embodiments, the external module is used as a communication repeater, and data is transmitted from the communication repeater to a remote cloud-based platform, and signal processing and analysis are performed in the remote platform.

[0169] According to some exemplary embodiments, the initial stage of signal processing occurs in a nearby external module, which allows, for example, to compress the data before transmission to the remote platform, thereby reducing the bandwidth required to transmit the data. In some embodiments, such compression can be achieved, for example, by averaging multiple repetitions of the signal acquired under the same or similar conditions, or by applying a transformation that allows the amount of data required to be reduced. For example, a fast Fourier transform (FFT) or discrete cosine transform (DCT) or other similar transformation can be performed, and data in frequency bands that are not required for subsequent signal processing and analysis can be discarded. In some embodiments, the data is compressed by downsampling the signal, that is, reducing the sampling rate, thereby allowing some data to be discarded. In some embodiments, specific signal processing is performed on data sampled at a higher rate, such as estimating the shape of a high-frequency transient, or estimating the frequency or amplitude of a high-frequency component in the frequency domain. In the examples and some embodiments, initial processing can be performed on a nearby external module on a signal acquired at an original, higher sampling rate, followed by downsampling the signal and transmitting the data to the remote platform at a lower sampling rate, thereby compressing the transmitted data and reducing bandwidth requirements.

[0170] According to some exemplary embodiments, device 204 is configured to measure symptom levels and / or changes in symptom levels of a neurological disease or condition, such as depression, Parkinson's disease, essential tremor, dystonia, epilepsy, obsessive compulsive disorder, addiction, chronic pain, cluster headaches, dementia, Huntington's disease, multiple sclerosis, stroke, Tourette syndrome, and traumatic brain injury. In some embodiments, device 204 is configured to measure symptom levels and / or changes in symptom levels based on signals received from one or more sensors connectable to device 204. Additionally or alternatively, device 204 is configured to measure side effect levels or changes in side effect levels of a brain stimulation therapy based on signals received from one or more sensors. In some embodiments, some side effects include one or more of the following: gaze deviation and double vision, unclear speech clarity (dysarthria) or poor speech volume control, persistent activation (dilation) of leg, arm or facial muscles, involuntary movements (dyskinesia), impaired balance, paresthesias (abnormal skin sensations such as tingling, tingling, cold, burning or numbness) due to problems with the function of the vestibular apparatus, acute emotional reactions such as acute mania or depression, impaired impulse control, changes in heart rate, changes in blood pressure, nausea / vomiting, and paracetamol (perception of flashes of light).

[0171] According to some exemplary embodiments, one or more sensors connectable to the device 204 include at least one body sensor 208 configured to be attached to the subject's body, for example to allow sensing directly from the subject's body. In some embodiments, the one or more body sensors include an electromyographic sensor, a magnetometer, an accelerometer, a gyroscope, a heartbeat sensor, a hemoglobin oxygen saturation sensor, a blood pressure sensor, an ECG sensor, an EEG sensor, a neuromuscular transmission sensor, a skin electrical activity (or skin conductivity) sensor, a respiratory monitor, a thermometer. In some embodiments, the one or more body sensors are configured to be positioned on the subject's 228 head, for example on the subject's face 209. Alternatively or additionally, the one or more body sensors are configured to be positioned on the subject's 228 body, for example on a limb 211 of the subject's 228. Optionally, the body sensor is placed on a sticker or includes a sticker that can be adhesively attached to the subject's body.

[0172] According to some exemplary embodiments, the one or more sensors connectable to device 204 include at least one optical sensor, such as a camera. In some embodiments, the optical sensor is configured to sense posture and / or movement of the subject's body.

[0173] According to some exemplary embodiments, the one or more sensors connectable to device 204 include at least one environmental sensor configured to sense the environment or changes in the environment surrounding the subject, such as an audio sensor configured to capture the subject's voice.

[0174] According to some exemplary embodiments, one or more sensors are electrically connected to the device 204 via a signal processing circuit, such as a signal processing circuit 214. In some embodiments, the signal processing circuit 214 is electrically connected to the control circuit 206 of the device 204. In addition, the device 204 includes a memory, such as a memory 216, electrically connected to the control circuit 206. In some embodiments, the signal processing circuit 214 is configured to process signals from the one or more sensors according to at least one signal processing algorithm and / or signal processing method stored in the memory 216. In some embodiments, for example, when the signal received from at least one sensor is an analog signal, the signal processing circuit 214 is used to convert the analog signal into a digital signal. Additionally or alternatively, the signal processing circuit is configured to amplify the signal received from the one or more sensors. Additionally or alternatively, the signal processing circuit is configured to evaluate and / or indicate the quality of the measurement, for example by measuring the impedance between the electrode and the patient's tissue. Additionally or optionally, the signal processing circuit is configured to evaluate and / or indicate the quality of the signal obtained over time, for example to detect signals with high amplitude transients associated with external noise that may corrupt the measurement results, or to calculate a signal-to-noise ratio measurement. Optionally, the signal processing circuit can reject low-quality signals as inputs to a signal processing chain used to assess the patient's condition. Additionally or optionally, the signal processing circuit is configured to estimate the subject's current activity, such as resting, walking, talking, performing one of several predefined tasks required for patient condition assessment, etc. Optionally, the estimate of the current subject's activity is used to determine which subsequent signal processing and analysis chains should be applied to the acquired signals. Alternatively and optionally, the estimate of the current subject's activity is fed as an additional input to subsequent multiple signal processing chains, so that the signal processing chains use different signal processing parameters or methods based on the current subject's activity. In some embodiments, the signal received from at least one sensor or an indication thereof is stored in memory 216. Additionally or alternatively, the processed signal or an indication thereof is stored in memory 216.

[0175] According to some exemplary embodiments, the device 204 includes an analysis circuit, such as an analysis circuit 218, electrically connected to the control circuit 206. In some embodiments, the analysis circuit is configured to analyze the stored processed signals or an indication thereof, for example to measure at least one side effect of the treatment and / or at least one disease symptom. In some embodiments, the analysis circuit is configured to analyze the stored processed signals using at least one algorithm stored in the memory 216, such as a machine learning algorithm. In some embodiments, based on the analysis of the stored processed signals, the analysis circuit 218 calculates each measured side effect score or an overall side effect score for the treatment. Alternatively or additionally, based on the analysis of the stored processed signals, the analysis circuit 218 calculates each measured symptom score for the neurological disease or neurological symptom, or an overall symptom score.

[0176] According to some exemplary embodiments, the analysis circuit 218 generates a quantitative assessment of a subject's condition based on one or both of the calculated side effect score and the calculated symptom score, for example as a subject condition score. In some embodiments, the analysis circuit 218 generates the subject's condition quantitative assessment using at least one algorithm, such as a machine learning algorithm stored in the memory 216. In some embodiments, the calculated side effect score, the calculated symptom score, and / or the subject's condition quantitative assessment are stored in the memory 216.

[0177] According to some exemplary embodiments, when the device 204 communicates with a DBS system or an IPG, the memory 216 stores a log file of the DBS system or the IPG. Alternatively or additionally, the memory 216 stores at least one DBS protocol or a parameter value thereof. In some embodiments, the analysis circuit is configured to determine a treatment space of the DBS treatment based on at least some of the stored log files, the stored DBS protocols, and / or the stored parameter values ​​of the DBS protocols.

[0178] According to some exemplary embodiments, the device 204 includes a user interface 220, which is configured to generate and transmit at least one indication to the subject receiving treatment and / or a specialist, such as a doctor or nurse. In some embodiments, the user interface 220 includes a display and / or a speaker. In some embodiments, the indication is related to one or more of the calculated side effect score, the calculated symptom score, the quantitative assessment of the subject's condition, and / or the determined treatment window. In some embodiments, the indication is a human-detectable indication, such as an audio indication or a visual indication.

[0179] According to some exemplary embodiments, if the quantitative assessment of the subject's condition indicates that the subject's condition is not within the desired treatment window, the indication is communicated to the subject and / or a specialist, such as an alarm signal. Alternatively, or additionally, an alarm signal is sent, such as when it is necessary to modify the DBS treatment, such as stopping the treatment or modifying one or more treatment parameter values.

[0180] According to some exemplary embodiments, the user interface 220 includes one or more input interfaces, such as buttons, a keyboard, or any input interface configured to allow data to be inserted into the device 204 and / or activate at least one function of the device 204. In some embodiments, a subject receiving DBS therapy activates the device 204 using the user interface 220 and performs a quantitative assessment of the subject's condition after the subject feels a side effect associated with the start of treatment.

[0181] According to some exemplary embodiments, the device 204 includes a communication circuit 222 electrically connected to the control circuit 206. In some embodiments, the communication circuit 222 is configured to send and receive signals from a remote device, such as from a pulse generator 224 of a DBS system. In some embodiments, the pulse generator 224 delivers electrical pulses to the brain of a subject 228 through electrode leads, such as lead 226. In some embodiments, the communication circuit is configured to receive and / or send wireless signals, such as Bluetooth, Wi-Fi, or any type of wireless signal to a DBS system, such as to the pulse generator 224.

[0182] According to some exemplary embodiments, the communication circuit 222 is used as a programmer of the DBS system, for example, as a programmer of the pulse generator 224. Alternatively, the device 204 is connected to a programmer of the DBS system, for example, the programmer 221. In some embodiments, the user selects one or more suggested combinations of treatment parameter values ​​suggested by the device 204, and the device 204 sends the information to the programmer 221 or the pulse generator, for example, via the communication circuit 222. Alternatively, the device 204 displays one or more suggested combinations of treatment parameter values ​​to a programmer, and the programmer manually programs the DBS system, for example, via a programmer of the DBS system.

[0183] According to some exemplary embodiments, the device 204 receives a wireless signal from the DBS system, such as a wireless signal from the pulse generator 224 of the DBS system, when an electrical pulse is delivered to the subject 228, when delivery of the pulse begins, and / or when delivery of the pulse ends. In some embodiments, the control circuit 206 sends a signal to the analysis circuit in a time relationship to quantitatively assess the condition of the subject, such as when the wireless signal from the pulse generator is received or within a selected time period after receiving the wireless signal, such as within a time period of up to 2 hours, up to 1 hour, up to 30 minutes, up to 10 minutes, up to 5 minutes, up to 1 minute from receiving the wireless signal. In some embodiments, the control circuit 206 sends a signal to the analysis circuit 218 to quantitatively assess the condition of the subject within a time period of up to 2 hours, up to 1 hour, up to 30 minutes, up to 10 minutes, up to 5 minutes, up to 1 minute, or any intermediate time period, a shorter or longer time period from receiving a signal from the pulse generator 224 indicating that delivery of the electrical pulse is complete.

[0184] According to some exemplary embodiments, the device 204 is configured to reprogram the pulse generator 224, for example, when the delivered DBS therapy is not within the determined treatment space and / or when the quantitative assessment of the patient's condition indicates the occurrence of undesirable side effects. In some embodiments, for example, during reprogramming, the control circuit 206 sends a signal to the communication circuit to send a wireless signal to the pulse generator 224. In some embodiments, the signal sent includes information about a new DBS protocol or a new DBS therapy parameter value combination selected to transfer the effect of the therapy into the treatment space. In some embodiments, the signal sent includes information about initiating or terminating DBS therapy based on a change in the assessment of the patient's condition.

[0185] According to some exemplary embodiments, the device 204 communicates with a database, such as a database including at least one data set, via the communication circuit 222. In some embodiments, the database 229 is stored in a server or cloud storage. In some embodiments, the database 229 stores information about stimulation results for patients with different stimulation parameter values. In some embodiments, the database 229 includes information or indications about treatment effect modifiers and the effects of treatment effect modifiers on the treatment effects of stimulation treatments delivered using different stimulation parameters. In some embodiments, the database 229 includes information or indications about previously defined treatment spaces in different patients and / or optimization certainties in stimulation treatments for different patients.

[0186] According to some exemplary embodiments, the control circuit 206 applies different statistical methods and / or algorithms to the large data set stored in the database 229, for example, to generate scores and / or rankings of different treatment parameter values ​​based on the large data set. In some embodiments, the generated scores and / or rankings are presented to the user, for example, on a display connected to the user interface, for example, to an expert using the user interface 220.

[0187] According to some exemplary embodiments, an external data processor, such as the data processor 230, applies different statistical methods to the large data set stored in the database 229, such as generating scores and / or rankings of different treatment parameter values ​​based on the large data set. In some embodiments, the device 204 receives the calculation results from the data processor, such as via the communication circuit 222. In some embodiments, the calculation results, such as scores and rankings, are transmitted to the user through a user interface.

[0188] According to some exemplary embodiments, the user selects a set of treatment parameter values ​​based on the provided scores and rankings and the results of the patient's condition. In some embodiments, the user reprograms the pulse generator using the selected set of treatment parameter values. Alternatively, the user decides to move the electrode lead 226 to a different location within the brain.

[0189] According to some exemplary embodiments, the control circuit 206 is configured to quantify an expected future flexibility, such as an expected buffer space. In some embodiments, the control circuit 206 is configured to quantify the expected future flexibility according to a specific treatment parameter value set and / or according to a specific stimulation position. In some embodiments, the control circuit 206 quantifies the expected future flexibility based on a set of specific treatment parameter values ​​stored in the memory 216. Alternatively or additionally, the control circuit 206 quantifies the expected future flexibility based on a patient condition assessment result.

[0190] According to some exemplary embodiments, the control circuit 206 quantifies the expected future flexibility, such as a score, based on the value of at least one treatment effect modifier stored in the memory 216. In some embodiments, the control circuit 206 uses at least one algorithm or statistical method stored in the memory 216 to quantify the expected future flexibility.

[0191] According to some exemplary embodiments, the control circuit 206 quantifies the desired future flexibility, such as a score, based on the value of at least one treatment effect modifier stored in the database 229. In some embodiments, the score is received from the data processor 230, for example, via the communication circuit 222. Alternatively, the score is received from the user via the user interface 220. In some embodiments, the control circuit 206 is configured to generate a treatment space based on the quantified future flexibility. In some embodiments, the control circuit sends a signal.

[0192] According to some exemplary embodiments, the control circuit 206 sends a signal to the user interface 220 to deliver a visual indication, such as to display one or more of the quantified results of the expected future flexibility and / or the score of at least one treatment effect modifier. Additionally or alternatively, the control circuit 206 sends a signal to the user interface 220 to deliver a visual indication, such as to display the generated treatment space.

[0193] According to some exemplary embodiments, the control circuit 206 is configured to send a signal to the user interface 220 to display the results of the desired future flexibility quantification, the score of at least one treatment effect modifier, and one or more of the treatment spaces via at least one graphical representation, such as a chart, spider diagram, table, or graph.

[0194] According to some exemplary embodiments, the control circuit 206 is configured to calculate a score and / or ranking for each of the at least two sets of treatment parameter values ​​based on the quantification of the future flexibility of each set of treatment parameter values. In some embodiments, the control circuit 206 is configured to signal the user interface 220 to generate and transmit a visual indication, such as to display the scores and / or rankings of the at least two sets of treatment parameter values.

[0195] According to some exemplary embodiments, the control circuit 206 is configured to update an existing future flexibility and / or an existing treatment space stored in the memory 216. In some embodiments, the control circuit 206 updates the existing future flexibility and / or the existing treatment space based on at least one quantitative assessment of the patient's condition performed, for example, at home or in the clinic.

[0196] Alternatively or additionally, the control circuit 206 updates the existing future flexibility and / or the existing treatment space based on at least one indication received, for example, from a remote computer or remote server via the communication circuit 222, for example, an indication associated with at least one treatment effect modifier.

[0197] According to some exemplary embodiments, if the updated future flexibility is not a desired future flexibility, for example, if the updated future flexibility indicates that the provided therapy does not have a desired therapeutic effect and / or will cause undesirable side effects, the control circuit 206 sends a signal to the user interface 220 and / or the communication circuit 222 to generate and transmit a human-detectable indication, such as an alarm signal, and transmits it to the patient or a person monitoring the patient's condition. Alternatively or additionally, if the size and / or shape of the updated treatment space is such that the provided therapy does not have a desired therapeutic effect and / or will cause undesirable side effects, the control circuit 206 sends a signal to the user interface 220 and / or the communication circuit 222 to generate and transmit a human-detectable indication, such as an alarm signal, and transmits it to the patient or a person monitoring the patient's condition.

[0198] According to some exemplary embodiments, the device 204 is a sensor cartridge, such as an all-in-one sensor cartridge, wherein one or more sensors are connected or attached to the cartridge, such as Figure 2A The sensor described in .

[0199] Illustrative Future Considerations for Stimulation Therapy

[0200] Reference now Figure 2B , which depicts different future considerations for stimulation therapy and general treatment protocols according to some exemplary embodiments of the present invention.

[0201] According to some exemplary embodiments, various future considerations are addressed when selecting a set of treatment parameter values ​​for a patient, for example to ensure that the delivered therapy remains effective within the desired treatment space, in the future, for example within a month, within a year, within 10 years, or any intermediate, shorter time period or longer, after electrode implantation.

[0202] According to some exemplary embodiments, various future considerations are addressed when selecting a set of treatment parameter values ​​for a patient, such as after electrode implantation to ensure that the delivered therapy remains effective within the desired treatment space in the future, such as within a month, within a year, within 10 years, or any intermediate, shorter or longer time period.

[0203] According to some exemplary embodiments, future considerations include at least one treatment effect modifier that has the potential to affect the treatment effect on the patient. In some embodiments, the at least one treatment effect modifier includes a disease symptom 262, for example, when the patient is in surgery, the expected change in the disease symptom over time may affect the treatment effect provided by the currently determined parameter value.

[0204] According to some exemplary embodiments, the at least one therapeutic effect modifier includes a medication regimen 260, such as changes in a patient's medication regimen over time. In some embodiments, future changes in the medication regimen may alter the patient's response to the stimulation therapy.

[0205] According to some exemplary embodiments, the at least one therapeutic effect modifier includes a healing process 258, such as the healing process of brain tissue after electrode lead implantation surgery. In some embodiments, the healing process can affect tissue near at least one stimulation electrode and optionally change the response of the tissue to the delivered stimulation.

[0206] According to some exemplary embodiments, the at least one therapeutic effect modifier includes a stimulation location 256. In some embodiments, the stimulation location can be changed over time, for example, to account for other changes caused by one or more therapeutic effect modifiers. In some embodiments, changing the stimulation location can affect the therapeutic effect, for example, reduce the therapeutic effect.

[0207] According to some exemplary embodiments, the at least one therapeutic effect modifier includes disease progression 254. In some embodiments, disease progression 254 is independent of or dependent on the stimulation therapy provided. In some embodiments, disease progression changes due to the stimulation therapy delivered. In some embodiments, disease progression or changes in disease progression result in optional changes in future treatment parameter values.

[0208] According to some exemplary embodiments, the at least one therapeutic effect modifier comprises a plurality of stimulation parameter values ​​252. In some embodiments, when currently selecting a therapeutic parameter value, planned changes to the stimulation parameter in the future need to be accounted for.

[0209] According to some exemplary embodiments, the at least one treatment effect modifier includes a plurality of treatment side effects 250, such as changes in treatment side effects over time. In some embodiments, when currently selecting treatment parameter values, known changes in future side effects need to be addressed.

[0210] According to some exemplary embodiments, one of the future considerations is optimization certainty 264. In some embodiments, optimization certainty refers to the certainty of completing the optimization process for treatment parameter value selection within a finite time period of an implantation procedure when starting the optimization process with a particular set of initial treatment parameter values.

[0211] According to some exemplary embodiments, the treatment effect modifiers and / or optimization certainties are scored at block 266. In some embodiments, the treatment effect modifiers and / or optimization certainties for a particular treatment parameter value, such as a treatment parameter value within a treatment space, are scored. In some embodiments, each modifier is scored independently. Alternatively, an overall score is calculated for all relevant treatment effect modifiers for a particular set of treatment parameter values. Optionally, the score for the optimization circuit is included in the overall score for a particular set of treatment parameter values.

[0212] According to some exemplary embodiments, the scores generated for the different treatment parameter value combinations are ranked at block 268. In some embodiments, the different treatment parameter value combinations are ranked according to the score generated for each combination.

[0213] According to some exemplary embodiments, at block 270, the rankings and / or scores are presented to a user, such as an expert.

[0214] According to some exemplary embodiments, at block 272, the user selects a particular combination of therapy parameter values ​​to reprogram the IPG. In some embodiments, the user selects a particular combination based on a ranking and / or score.

[0215] Exemplary system for quantitatively assessing patient condition

[0216] Reference now Figure 3 , Figure 3 A system for quantitatively assessing a patient's condition according to some exemplary embodiments of the present invention is depicted.

[0217] According to some exemplary embodiments, a system for quantitatively assessing a patient condition, such as system 302, includes one or more sensors, such as sensors 304 and 306, and one or more acquisition modules, such as acquisition modules 308 and 310, electrically connected to sensors 304 and 306, respectively. In some embodiments, the communication module and storage module and processing module of the system (such as system 302) are used to perform its functions, such as quantitatively assessing the patient condition. According to some exemplary embodiments, sensors, such as sensors 304 and 306, are connected to acquisition modules, such as acquisition modules 308 and 310, which perform initial signal conditioning on analog signals and digitize them in A2D. In addition, the recorded data is sent to a processor, such as processor 312, which obtains instructions on how to perform signal processing from a memory module, such as memory 314.

[0218] According to some exemplary embodiments, a system, such as system 302, optionally further includes one or more of camera recording, voice recording, EMG signal recording, EO signal recording, EEG recording, position and / or direction recording, heartbeat recording, hemoglobin oxygen saturation recording, blood pressure recording, electrocardiogram recording, neuromuscular conduction recording, skin conductivity recording, respiration recording, body temperature recording and / or one or more gaze tracking devices, as described in detail below.

[0219] Optionally, the system also includes a display to present the results to a system operator, such as a subject or a clinician, and a user interface, such as display and user interface 316, for user interaction with the system.

[0220] It should be noted that in some embodiments, different sensor types provide information for quantifying the same attribute, such as symptoms, clinical signs, side effects of treatments, whether pharmacological, electrical, or other. When describing "sensor X is used to quantify attribute Y", it should be understood that in some embodiments, sensor X is used alone to quantify attribute Y, or in other embodiments is used in conjunction with other sensors to quantify attribute Y. In the latter case, in some embodiments, the metrics from the various sensors are fused together into a single metric, such as by averaging - which can be a simple or weighted average - or by a decision tree, or by another well-known method of fusing various metrics into a single metric.

[0221] According to some exemplary embodiments, the system is operable to perform a normalization or standardization step on each metric, e.g., to bring the various metrics to a similar scale so that they can be averaged in a meaningful manner. For example, suppose that an EMG-based stiffness measurement is found to typically vary between 20 and 50 microvolts, and that a stiffness sensing motion module typically produces values ​​that vary between 0 and 5 nanoseconds / meter. The first metric is then optionally normalized by subtracting 20 and then dividing by 30 to bring it to a scale of 0-1, while the second metric is divided by 5 to bring it to a scale of 0-1, and the two metric values ​​are then added together to be averaged.

[0222] Alternatively, the metrics generated from the various sensors are fed as input to a statistical inference calculation stored in memory 314, such as one performed by a machine learning prediction algorithm, which maps the combination of input metrics to a single output metric.

[0223] According to some exemplary embodiments, the system 302 includes a signal processing module, such as a signal processing module 318 electrically connected to one or more acquisition modules (such as the acquisition module 308). In some embodiments, the signal processing module 318 is configured to process the signals received from the sensor by one or more of filtering, envelope detection, and spectrum estimation (including mel spectrum and discrete cepstrum estimation) to detect peaks and calculate peak prominence values, calculate various statistical measures of the signal, such as calculating the mean, standard deviation, median, signal range and interquartile range, calculate correlation between signals from the same source or different sources, calculate cross-correlation between signals from the same source or different sources, align a signal with a trigger signal in time, average two or more time-aligned signals, or subtract one signal from another, detect high-amplitude transient artifacts and selectively remove them, perform impedance measurements, and provide signal quality estimates. The main function of the signal processing module is to verify the quality of the input signal and adjust the signal to make it more suitable for analysis, for example, filtering out noise through a low-pass filter, or eliminating noise through a high-pass filter or other methods. In addition, the signal processing module applies techniques that highlight signal features that are important for analysis, such as by applying a transform to a frequency representation or a time-frequency representation, in which it is easier to estimate spectral features or spectral features that vary over time. Alternatively, features of interest are highlighted by averaging two or more repetitions of the same type of signal, thereby generally increasing the signal-to-noise ratio, or by subtracting one signal from another signal, or by subtracting a group of signals from another signal set, thereby eliminating common-mode signal features and highlighting the differences between the feature signals. Before performing such subtraction or averaging, alignment is usually required to ensure that the delay in signal acquisition time is eliminated or at least taken into account. The ultimate goal of the signal processing module is to calculate an output quantity or signal, which can be output to a user or fed as an input to an index calculation module, which uses the input to provide an evaluation index. In some embodiments, the signal processing module 318 is configured to process signals received from sensors, such as obtaining one or more signal features, such as a numerical value or score calculated from the combination of one or more signal inputs, and used to calculate an index of a subject's attribute.

[0224] According to some exemplary embodiments, the system 302 includes an index calculation module, for example, an index calculation module 320 electrically connected to the processor 312. In some embodiments, the index calculation module 320 is configured to calculate the index using one or more algorithms stored in the memory 314, for example, by combining the signal features obtained by the signal processing module.

[0225] According to some exemplary embodiments, the system 302 includes a user input module, such as a user input module 322 electrically connected to the processor 312. In some embodiments, the user input module 322 includes at least one button, a keypad, or a keyboard. In some embodiments, the user input module is configured to allow receiving signals and / or information from a user of the system 302.

[0226] According to some exemplary embodiments, system 302 includes an interface 324 for inputting previous data electrically connected to memory 314 and configured to upload data from an external storage device to memory 314. In some embodiments, interface 324 includes a flash drive interface and / or a USB interface.

[0227] According to some exemplary embodiments, system 302 includes a graphical representation module 311 that is electrically connected to processor 312 and display and user interface 316. In some embodiments, the processor sends a signal to the graphical representation module to generate a graphical representation of a treatment space, a score or value of at least one treatment effect modifier, and / or a quantitative result of future flexibility. In some embodiments, the graphical representation module generates a graphical representation of a treatment space, a score or value of at least one treatment effect modifier, and / or a quantitative result of future flexibility. Figure 2A A graphical representation of the user interface 220 is described in .

[0228] Example Methods for Quantitative Assessment of Patient Condition

[0229] According to some exemplary embodiments, a method for quantifying some movement disorder symptoms and side effects, such as DBS-induced side effects, uses an array including at least one sensor of at least one type of sensor. In some embodiments, the sensor includes one or more of an EMG electrode, an EOG electrode, an eye tracking sensor, an audio recorder, a camera, and a rigidity sensing module having at least one accelerometer, at least one gyroscope, and / or at least one dynamometer.

[0230] According to some exemplary embodiments, sensors are applied to the subject or the subject's environment (e.g., an audio recorder is placed near the subject, an eye movement tracker is placed so as to be in direct line of sight with the subject's eyes, and a camera is placed and configured to record movements of the subject's limbs and face).

[0231] According to some exemplary embodiments, sensor data is recorded while the patient is at rest. Alternatively or additionally, sensor data is recorded while the patient is engaged in a task. In some embodiments, data is first recorded while the patient is at rest and then recorded during the engagement task, or vice versa. In some embodiments, recording during rest or during the engagement task is repeated more than once and the number of times the recording is performed is predefined or can be modified online by the results of previous recordings.

[0232] According to some exemplary embodiments, the engaging task includes, for example, performing at least one motor task, such as repeatedly tapping the index finger and thumb using one of the limbs. In some embodiments, the engaging task includes saying a set of syllables or words, and / or moving the eyeballs to each side. In some embodiments, the engaging task includes moving one of the patient's limbs via the device while the patient remains passive.

[0233] According to some exemplary embodiments, the patient performs a complex task, which optionally includes eye movements, limb movements and / or vocal movements. In some embodiments, the complex task is performed interactively with a computer display, such as a touch-sensitive tablet computer, on which instructions are displayed explicitly or implicitly. For example, in some embodiments, the subject is required to look at a moving mark "A" on the display, tap it when its appearance changes to mark "B", and clearly speak when it changes to mark "C". According to some exemplary embodiments, the sensor signal is processed to obtain signal characteristics. In some embodiments, for each attribute, an index is calculated by combining the signal characteristics according to an equation.

[0234] Reference now Figure 4A , Figure 4A A process for quantitatively assessing patient symptoms after task performance according to some exemplary embodiments of the present invention is depicted.

[0235] According to some exemplary embodiments, at block 402, one or more sensors are applied to a patient or the patient's environment. In some embodiments, the sensors include one or more of a body sensor, an optical sensor, and an environmental sensor, such as Figure 2A As described in.

[0236] According to some exemplary embodiments, at box 404, signals from the sensors are recorded while the patient is at rest, such as when the patient is not engaging in any physical and / or cognitive activity, such as producing movement of the patient or resisting movement imposed on the patient by an external source.

[0237] In some embodiments, the patient is instructed to remain still and relaxed, not to move, and not to assist and not to resist attempts by someone or other object to move the patient's body.

[0238] According to some exemplary embodiments, at box 406, signals from sensors are recorded while the patient engages in a task. In some embodiments, the task includes performing at least one motor task, for example, using one of the limbs, repeatedly tapping the index finger and thumb against each other, opening and closing a fist, maintaining the arm in the air, bringing a cup to the mouth, or moving the hand in a spiral. In some embodiments, engaging in the task includes saying a set of syllables or words, and / or moving the eyeballs to each side. In some embodiments, engaging in the task includes moving one of the patient's limbs through a device while the patient remains passive. In some embodiments, engaging in the task includes walking or running, standing steadily without moving, or being pushed or pulled and regaining balance. In some embodiments, the task is engaged in in conjunction with cognitive challenges such as performing arithmetic calculations.

[0239] According to some exemplary embodiments, the patient performs a complex task, which optionally includes eye movements, limb movements, and / or vocal movements. In some embodiments, the complex task is performed interactively with a computer display, such as a touch-sensitive tablet computer, on which instructions are displayed explicitly or implicitly. For example, in some embodiments, the subject is required to look at a moving marker "A" on the display, tap it when its appearance changes to marker "B", and articulate verbally when it changes to marker "C".

[0240] According to some exemplary embodiments, the task is selected to induce the onset of at least one side effect of the treatment and / or at least one disease symptom. For example, it is known that the stiffness of one hand in Parkinson's disease often increases when the other hand is used, such as when a fist is opened and closed.

[0241] Another example is that a hand tremor in "essential tremor" often occurs when the patient attempts to perform a precise task with that hand, such as drinking alcohol or touching the clinician's fingers with their own. In contrast, in Parkinson's disease, the patient is prone to tremors at rest that often lessen or eliminate when they initiate movement.

[0242] According to some exemplary embodiments, at block 408, the rest and task-related signals are processed to compute at least one feature, such as a sign, symptom, and / or side effect. In some embodiments, the rest-related signals are processed to quantify Parkinson's tremor, rigidity, internal capsule recruitment, and / or posture. In some embodiments, the task-related signals are processed to quantify bradykinesia, gaze palsy or diplopia, dysarthria or abnormal voice volume control, and / or gait disturbance.

[0243] According to some exemplary embodiments, at block 410, an index is calculated for each feature. In some embodiments, an index, such as a score, is calculated for each feature. In some embodiments, the index is calculated using one or more algorithms stored in a memory of an evaluation device, such as, Figure 2A The memory 216 or Figure 3 The memory 314 shown. In some embodiments, for example, by Figure 2A The analysis circuit 218 shown in FIG. Figure 3 The index is calculated by the index calculation module 320 shown in FIG.

[0244] According to some exemplary embodiments, an overall score of the patient's condition is calculated at block 412. In some embodiments, the overall score is calculated based on the index calculated for each feature. In some embodiments, the overall score is calculated by a processor, control circuitry, or analysis circuitry of the evaluation device. In some embodiments, the overall score is calculated using at least one algorithm stored in a memory of the device.

[0245] Reference now Figure 4B , Figure 4B Depicted are some exemplary embodiments of the present invention, based on information from a large data set to a quantitative assessment of a patient's condition. In some embodiments, statistical inference and / or machine learning or any other classification, indexing, processing, scoring method described in this application is used to generate information based on a large data set.

[0246] According to some exemplary embodiments, at block 414, sensor data from a plurality of patients is recorded during rest and while performing tasks.

[0247] According to some exemplary embodiments, at block 416, signal features of the sensor data recorded at block 414 are calculated. In some embodiments, the signal features are data extracted from at least one stored signal. In some embodiments, the signal features are, for example, at least one signal in raw form. Alternatively or additionally, the features include a pre-processed form, such as after at least one of filtering, mean subtraction, artifact suppression or removal, noise cleaning, or similar processing methods that improve the usability of the signal without significantly compressing its size or changing its properties.

[0248] According to some exemplary embodiments, the features are parameters extracted from the signal, such as mean, median, variance, standard deviation, statistical skewness, kurtosis or other high-order statistical measures, discrete cosine transform (DCT) components and / or entropy. In some embodiments, the spectral domain features include one or more of the frequency of the highest spectral power component, the amplitude of the highest spectral power component, total harmonic distortion, power in one or more frequency bands, which can be calculated as a fraction of the power spectral density of the signal between two edges of the frequency band, a statistical characteristic or measure of the power spectral density.

[0249] According to some exemplary embodiments, the features are constructed from the time-frequency representation of the signal, such as the short-time Fourier transform, other Fourier-based spectrograms, such as Welch's spectral estimation-based, wavelet transform, Wigner-Ville transform, or similar transforms. In some embodiments, the features are constructed from the entire time-frequency representation, from at least one selected segment in the time-frequency representation, or from selected components of these representations, e.g., the amplitude on one or more frequency bands during one or more time intervals, or the duration or power of consecutive peaks or valleys in the time-frequency domain. Alternatively or additionally, other features are generated by cepstrum analysis (equivalent in some embodiments to applying spectral estimation to the logarithm of the power spectral density), including cepstrum coefficients and / or Mel-frequency cepstrum coefficients (MFCCs).

[0250] According to some exemplary embodiments, the features include parametric representations of the signal, such as autoregressive (AR) coefficients that optionally provide the best estimate of the signal, autoregressive moving average (ARMA) coefficients that optionally best estimate the signal, linear prediction coding coefficients, or other parametric representations. In some embodiments, the features are higher-order, i.e., composed of more than one signal, e.g., from two or more electromyography channels, or between at least one electromyography channel, at least one motion sensor (accelerometer, gyroscope, goniometer, or optical markers obtained from a camera for tracking a patient's movement), or between any two or more data channels.

[0251] In some embodiments, the higher-order features include the mutual information between signals, the correlation coefficient between signal pairs, the maximum cross-correlation value between two signals, the delay time between signals (e.g., estimated by the delay corresponding to the maximum cross-correlation value).

[0252] According to some exemplary embodiments, multiple features are derived from other features rather than directly from the signal. In some embodiments, multiple features are, for example, constructed by a dimensionality reduction method that combines multiple inputs (primary features), and the dimensionality reduction method optionally optimizes an objective function that generally attempts to concentrate "important information" in fewer components than the number of inputs. In some embodiments, assuming there are N inputs, which can be N features from the above list, there are typically also N outputs, but the objective function defines what "important information" is concentrated in M < N output components. In some embodiments, for example, the objective function of the principal component analysis (PCA) method defines data variance as important information, and in some cases, three principal components calculated by a linear combination of, for example, 100 inputs may be sufficient to account for 90% or 95% of the variance in the data. Thus, only three PCA components can be maintained, and these will be the features for subsequent analysis.

[0253] According to some exemplary embodiments, additional techniques for dimensionality reduction include non-negative matrix factorization (NMF), locally linear embedding (LLE), Laplacian stencil map, isovalue map, linear discriminant analysis, generalized discriminant analysis, maximum variance expansion, and diffusion mapping.

[0254] According to some exemplary embodiments, at block 418, human expert assessments of symptoms and side effects for a plurality of patients are provided. In some embodiments, a database of labeled data is constructed based on the human expert assessments. In some embodiments, the human expert assessments are used as references, e.g., "ground truth" labels that the algorithm attempts to match, e.g., by optimizing a combination of signal features. In some embodiments, as the process continues and the number of patients from which data is collected grows, accuracy improves.

[0255] According to some exemplary embodiments, at block 420, the relationship between the signal feature and each symptom and side effect is statistically inferred. In some embodiments, the relationship between the feature and the output is estimated. In some embodiments, the output is a human expert assessment, such as a binary variable (whether a side effect is present) or a categorical variable represented as a symptom. In some embodiments, for example, the symptoms of Parkinson's disease are assessed based on a rating scale, such as the Unified Parkinson's Disease Rating Scale (UPDRS), or a variant thereof, where each assessment is actually a classification of a symptom or side effect into one of the groups defined as 0, 1, 2, 3, and 4.

[0256] According to some exemplary embodiments, the method for estimating the relationship between features and outputs generally includes linear regression or regression analysis, logistic regression for binary variables, perceptrons and multi-person perceptrons, support vector machines, naive Bayes classifiers, K-nearest neighbor algorithms, decision trees and random forests, artificial neural networks (ANNs) including deep neural networks, recursive neural networks and convolutional neural networks, Bayesian networks including dynamic Bayesian networks (DBNs) and hidden Markov models (HMMs), genetic algorithms and evolutionary algorithms. In some embodiments, generally, algorithms and models attempt to optimize the correctness of the prediction of the correct output based on the input. Optionally, the prediction is optimized by a training algorithm, which repeatedly updates model parameters (such as connections between nodes in ANN, probability matrices in HMM, etc.) according to an update rule until convergence to the optimal value with the smallest prediction error. In some embodiments, for example, ANN is trained by back propagation techniques, HMM is trained by the Viterbi algorithm, and Bayesian networks are trained by belief propagation methods.

[0257] In some embodiments, in some of these models, it is explicitly established, such as in linear regression, how important each input feature is to improving predictions and minimizing error. In other techniques, such as artificial neural networks, it is not clear how each feature contributes to minimizing prediction error. In some embodiments, features can be ranked according to their impact on prediction error by removing input features, repeating the training process, and calculating prediction error without removing features. In some embodiments, the process is also performed on feature pairs, triplets of features, etc., because in some cases, the combination of features is more informative than the sum of their information values.

[0258] In some embodiments, the extent to which a feature or combination of features contributes to the prediction depends on other variables, such as the patient's disease, disease stage, age, primary symptoms, primary affected side, and additional or other medications. In some embodiments, by having a sufficiently large database, subgroups of the patient population can be evaluated for the most informative features, such as a specific combination of two or more of disease, disease stage, age, primary symptoms, primary affected side, and additional or other medications.

[0259] According to some exemplary embodiments, at box 422, the most informative signal feature type is selected. In some embodiments, the most informative refers to a set of feature types that are most useful in accurately calculating a specific index, for example, by testing previously obtained data. In some embodiments, this is based on obtaining previous data from the same patient, or previous data from other patients, or a database, where the data is also accompanied by multiple tags that are external rather than system-generated, and indicate the patient's condition. Typically, these labels can be provided by professional clinicians who have examined patients at the same time or under the same conditions as the system. Based on such tags, manual testing or automatic algorithms can be used to determine the most informative signal feature type for a specific combination of disease, disease stage, age, main symptoms, main affected side, and additional or other drugs. In some embodiments, the calculation method for calculating the features in box 416 and the list of the most informative signal feature types determined in box 422 are applied to the records from boxes 404 and 406 at box 408.

[0260] Alternatively or additionally, at box 424, the most informative signal features are used to update one or more index calculation formulas or algorithms. In some embodiments, the index calculation formula or algorithm is a specific formula, model or algorithm associated with the signal features and expert evaluation as described above. In some embodiments, after the most informative signal features are selected, the formula, model or algorithm trained according to the selected M input features becomes an updated index calculation formula or index calculation method. According to some exemplary embodiments, the updated one or more index calculation formulas or algorithms from box 424 are used for the index calculation at box 410. Therefore, during the evaluation process, the index of the current specific patient is calculated using an index calculation method constructed and trained on a database consisting of data obtained from the same patient and / or other patients in the past.

[0261] Exemplary quantitative assessment of Parkinson's disease symptoms and treatment side effects

[0262] Reference now Figure 5 , Figure 5 Depicted are processes for quantitatively assessing symptoms and / or side effects of treatment of neurological diseases, such as Parkinson's disease, according to some exemplary embodiments of the present invention.

[0263] According to some exemplary embodiments, at least one sensor is applied to a patient or a patient's environment at block 502. In some embodiments, the at least one sensor includes one or more of a body sensor, an optical sensor, and / or an environmental sensor.

[0264] According to some exemplary embodiments, at block 504, a signal is recorded by at least one sensor while the patient is at rest.

[0265] According to some exemplary embodiments, at block 506 , signals are recorded by at least one sensor while the patient engages in the task.

[0266] According to some exemplary embodiments, the rest and task related signals are processed at block 508. In some embodiments, the signals are processed, for example, to compute one or more features.

[0267] According to some exemplary embodiments, the calculated one or more features are used to calculate an index for each sign, symptom, and / or side effect at block 510. In some embodiments, specific indices are calculated, such as a tremor index 512, a bradykinesia index 514, a rigidity index, a fixation index 518, a recruitment index, a dyskinesia index, and / or a voice and dysarthria index 524.

[0268] According to some exemplary embodiments, at block 526, an overall score for the patient's condition is calculated. In some embodiments, the overall score is calculated based on at least some specific index.

[0269] Exemplary Programming Pulse Generator

[0270] According to some exemplary embodiments, a pulse generator, such as an implantable pulse generator (IPG), is programmed based on a quantitative assessment of the patient's condition. In some embodiments, the IPG is automatically programmed by a device or system for assessing the patient's condition. Alternatively, the IPG is manually programmed by an expert, such as a physician or nurse, based on recommendations, such as recommended treatment parameter values ​​communicated to the expert by an assessment device.

[0271] According to some exemplary embodiments, a method for programming an IPG for transmitting DBS includes the following steps.

[0272] According to some exemplary embodiments, data from a DBS implant surgery and a previous programming period (if any) - prior data is received. In some embodiments, the prior data includes electrophysiological recordings from the surgery and / or the processed output of these recordings, and the recorded trajectories are mapped to functional areas, as described, for example, in US8792972 or WO2018008034. According to some exemplary embodiments, the prior data is used to plan an efficient search of the DBS parameter space. In some embodiments, the search includes identifying DBS lead contacts that are located in statistically less favorable positions and should not be tested or should be tested relatively sparsely, and optimally located contacts that should be tested at high resolution. In some embodiments, the plan includes which DBS configurations will be tested.

[0273] According to some exemplary embodiments, the plan is presented to the patient's caregiver and approved.

[0274] According to some exemplary embodiments, at least one sensor is applied to the patient and / or the patient's environment, for example to record one or more of speech, eye movement, muscle activation, and mechanical strength of rigidity.

[0275] According to some exemplary embodiments, all variables are recorded under baseline conditions, where the patient is off treatment or receiving baseline treatment.

[0276] According to some exemplary embodiments, an initial scanning process is initiated. According to some exemplary embodiments, DBS parameters are adjusted to the selected planning configuration. In some embodiments, a DBS treatment is delivered to the patient using the selected configuration.

[0277] According to some exemplary embodiments, data from all sensors is recorded. In some embodiments, various analysis methods are applied to the recorded data, for example, to obtain an index of one or more of various symptoms, signs, and side effects. Optionally, the analysis results and / or the index are used to adjust the scanning plan. In some embodiments, if one or more contact configurations that are expected to be highly beneficial cause side effects under relatively low current stimulation, higher voltage or finer granularity testing of the contacts can be cancelled. Additionally or alternatively, contacts or contact configurations that were initially thought to produce poor desired results can be scanned in finer detail to search for the best results. In some embodiments, once data recording with a first DBS parameter configuration is completed, the configuration is changed to a different configuration, DBS treatment is delivered and data from the sensors is measured. In some embodiments, the DBS configuration is changed until the final planned configuration is reached.

[0278] According to some exemplary embodiments, the scan results are presented, for example, in the form of a table summarizing the quantification of the properties at each tested configuration, and / or by a higher level graphical representation optionally highlighting the onset of symptom relief and side effects for each of all or selected configurations. Optionally, a set of optimal configurations is presented, for example comprising a combination of at least 2, 3, 4, 5, 6 or any smaller or larger configurations.

[0279] Reference now Fig. 6A , Fig. 6A Programming a pulse generator using a quantitative assessment of a patient's symptoms is depicted in accordance with some exemplary embodiments of the present invention.

[0280] According to some exemplary embodiments, sensors are applied to a patient and / or the patient's environment at block 602. In some embodiments, the sensors include one or more of a body sensor, an optical sensor, and an environmental sensor.

[0281] According to some exemplary embodiments, at block 604, pulse generator parameters are set. In some embodiments, once the parameters are set, DBS therapy is delivered to the patient.

[0282] According to some exemplary embodiments, at block 608, sensor signals are recorded while the patient is at rest.

[0283] According to some exemplary embodiments, at block 610 , sensor signals are recorded while the patient engages in a task.

[0284] According to some exemplary embodiments, at block 610, signals recorded during rest periods and during engagement in a task are processed.

[0285] According to some exemplary embodiments, an index is calculated for each sign, symptom, or side effect at block 612. In some embodiments, the index is calculated based on the processed signal.

[0286] According to some exemplary embodiments, once the index is calculated, a different set of values ​​are used to set the pulse generator parameters at block 604. In some embodiments, the signal recording, signal processing, and calculation of a new index for each sign, symptom, or side effect are repeated as the patient undergoes a DBS treatment with a new combination of parameter values.

[0287] According to some exemplary embodiments, at block 614, different settings of the pulse generator are ranked based on the calculated index.

[0288] According to some exemplary embodiments, the ranking is presented to a user.Alternatively, the pulse generator is automatically programmed according to the selected settings, for example the setting with the highest ranking.

[0289] According to some exemplary embodiments, for example Figure 6B As shown, at block 618, previous data from previous assessments and operating room electrophysiology is retrieved. In some embodiments, the previous data is stored in a memory of the assessment device.

[0290] According to some exemplary embodiments, at block 603, initial pulse generator parameters are set based on stored previous data. In some embodiments, once the parameters are set, DBS is delivered to the patient using the initial pulse generator parameters.

[0291] According to some exemplary embodiments, once the index is calculated at block 612, the next set of parameters is calculated based on previous data and / or previous results in the current period at block 620. In some embodiments, the next set of parameters is an optimal parameter combination, in the sense that this parameter combination is most likely to be the most effective parameter combination selected at this stage and minimizes the number of subsequent parameter combinations to be tested before reaching the optimal parameter combination, thereby allowing the DBS treatment to have maximum therapeutic effect and minimum side effects.

[0292] According to some exemplary embodiments, the pulse generator is set up with the next set of parameters at block 622. In some embodiments, once the pulse generator is programmed with the next set of parameters, DBS is delivered to the patient.

[0293] Example Index Calculation

[0294] Reference now Fig. 7A , Fig. 7A A general process for generating one or more indices according to some exemplary embodiments of the present invention is depicted.

[0295] According to some exemplary embodiments, at block 702, EMG electrodes are placed on a subject, such as a patient. In some embodiments, the EMG electrodes are placed at one or more locations on the patient's face. Alternatively or additionally, the electrodes are placed at one or more locations on at least one limb of the subject, such as a leg or hand.

[0296] According to some exemplary embodiments, at block 704, the subject is indicated to be in a resting state. In some embodiments, while the subject is resting, signals from the EMG electrodes are received and optionally processed and / or stored.

[0297] According to some exemplary embodiments, the subject is instructed to engage in a task at block 706. In some embodiments, while the subject is engaging in a task, signals from the EMG electrodes are received and optionally processed and / or stored.

[0298] According to some exemplary embodiments, a plurality of signal features are calculated at block 708. In some embodiments, the plurality of signal features are calculated from signals measured while the subject is at rest and / or from signals measured while the subject is engaged in a task.

[0299] According to some exemplary embodiments, one or more indices are calculated from the calculated plurality of signal features. In some embodiments, at block 708, a tremor index is calculated. In some embodiments, at block 710, a dyskinesia index is calculated. In some embodiments, at block 712, a rigidity index is calculated. In some embodiments, at block 714, a recruitment side effect index is calculated.

[0300] According to some exemplary embodiments, tremor-related signal components are separated from non-tremor-related signal components before calculating the signal features. Figure 7B , Figure 7B Depicted is a process for generating an index by isolating a tremor-related signal according to some exemplary embodiments.

[0301] According to some exemplary embodiments, at block 716 , after recording the signals of the subject at rest and during the task engagement, respectively, the signals (eg, signal components) associated with tremor and the signals associated with non-tremor are separated.

[0302] According to some exemplary embodiments, at block 718, a plurality of tremor signal features are calculated based on the tremor-related signal components separated at block 716. In some embodiments, at block 720, a tremor index is calculated. In some embodiments, the tremor index is calculated based on the calculated plurality of tremor signal features.

[0303] According to some exemplary embodiments, at block 716, the separated non-tremor signal components are used to calculate a plurality of non-tremor signal features at block 722. In some embodiments, at block 724, a dyskinesia index is calculated based on the calculated plurality of non-tremor signal features. In some embodiments, at block 726, a rigidity index is calculated based on the calculated plurality of non-tremor signal features. In some embodiments, at block 728, a recruitment side effect index is calculated based on the calculated plurality of non-tremor signal features.

[0304] According to some exemplary embodiments, non-tremor related indices, such as a dyskinesia index, a rigidity index, and a recruitment side effect index, are calculated separately from the calculated non-tremor related signals.

[0305] Demonstration Task Relevance Index Calculation

[0306] Reference now Figure 7C , Figure 7C Depicted is the calculation of a task-relevance index compared to a baseline, according to some exemplary embodiments of the invention.

[0307] According to some exemplary embodiments, at box 740, baseline measurements of one or more of disease symptoms, treatment side effects, and subject condition are started. In some embodiments, during the baseline measurement, at box 742, EMG or kinematic signals from the subject are recorded. In some embodiments, EMG or kinematic signals are recorded while the subject is at rest. Alternatively or additionally, the subject is instructed to emit a specific sound and then the emitted sound is recorded. Alternatively or additionally, the subject is instructed to perform eye movements while the system records eye position and / or tracks eye movements. According to some exemplary embodiments, after or during treatment delivery, or after a period of time selected from the baseline measurement, at box 748, test condition measurements are performed. According to some exemplary embodiments, for example, as described above, at box 750, the subject is instructed to perform a repetitive motor task. In some embodiments, the subject is instructed to perform a task during treatment delivery or within a period of up to 1 day, such as up to 12 hours, up to 10 hours, up to 5 hours, up to 1 hour, up to 30 minutes, or any intermediate, smaller or larger time period from the end of treatment delivery.

[0308] According to some exemplary embodiments, at block 752, EMG and / or kinematic signals are acquired. In some embodiments, the signals are acquired during the performance of the motor task. Alternatively, the signals are acquired for a duration of up to 1 day from the end of the performance of the motor task, such as up to 12 hours, up to 10 hours, up to 5 hours, up to 1 hour, up to 30 minutes, or any intermediate, smaller, or larger duration.

[0309] According to some exemplary embodiments, at block 754, one or more signal features are calculated. In some embodiments, the one or more signal features include frequency domain, fundamental frequency, or other signal features described in the "Exemplary Feature Construction" section. In some embodiments, multiple features are calculated based on EMG or kinematic signals recorded at rest, after or during performance of a motor task. In some embodiments, the calculated multiple features of the signal measured during or after the task are compared to the calculated multiple features of a baseline signal, such as a signal measured at rest.

[0310] According to some exemplary embodiments, at block 756, a bradykinesia index is calculated. In some embodiments, as described at block 754, the bradykinesia index is calculated based on a comparison with a baseline characteristic.

[0311] According to some exemplary embodiments, the subject is instructed to repeat the sound at block 758. In some embodiments, the subject is instructed to repeat the sound during treatment or for a period of up to 1 day, e.g., up to 12 hours, up to 10 hours, up to 5 hours, up to 1 hour, up to 30 minutes, or any intermediate, smaller, or larger period after treatment ends.

[0312] According to some exemplary embodiments, at block 760, the voice signal is recorded. In some embodiments, the signal is acquired during the sound emission. Alternatively, the signal is acquired within a duration of up to 1 day from the end of the sound emission, such as up to 12 hours, up to 10 hours, up to 5 hours, up to 1 hour, up to 30 minutes, or any intermediate, smaller or larger duration.

[0313] According to some exemplary embodiments, at block 762, a plurality of signal features are calculated. In some embodiments, the plurality of signal features are calculated based on the baseline signal and the speech signal recorded at block 760. In some embodiments, the calculated features of the baseline signal are compared to the calculated features of the signal recorded at block 760.

[0314] According to some exemplary embodiments, at block 764, a speech and / or articulation disorder index is calculated. In some embodiments, based on the comparison results performed at block 762, a speech and / or articulation disorder index is calculated.

[0315] According to some exemplary embodiments, the subject is instructed to repeatedly perform the eye movement at block 766. In some embodiments, the subject is instructed to repeatedly perform the eye movement during the treatment or within a time period of up to 1 day, e.g., up to 12 hours, up to 10 hours, up to 5 hours, up to 1 hour, up to 30 minutes, or any intermediate, smaller, or larger time period from the end of treatment delivery.

[0316] According to some exemplary embodiments, the eye position is tracked at block 768. In some embodiments, at block 766, the eye position is tracked during the execution of the eye movement. Alternatively, at block 766, the eye position is tracked for a duration of up to 1 day from the end of the eye movement, such as up to 12 hours, up to 10 hours, up to 5 hours, up to 1 hour, up to 30 minutes, or any intermediate, smaller, or larger duration.

[0317] According to some exemplary embodiments, an eye movement limit is calculated at block 770. In some embodiments, the eye movement limit is calculated based on a comparison between a baseline eye position and an eye position of repeatedly performing eye movements.

[0318] According to some exemplary embodiments, at block 772, a fixation index is calculated. In some embodiments, the fixation index is calculated based on the calculated eye movement limit. In some embodiments, the fixation index is calculated based on the difference between the eye position recorded at the baseline and the eye position after repeated eye movements.

[0319] Isolation of Exemplary Tremor-Related Signals

[0320] According to some exemplary embodiments, for example Figure 7B As shown, before calculating the signal characteristics and / or calculating at least one index, the signal recorded by at least one sensor is divided into a tremor-related signal and a non-tremor-related signal. FIG. 8A to FIG. 8C , depicting different methods for separating tremor-related signals from non-tremor-related signals according to some exemplary embodiments of the present invention.

[0321] According to some exemplary embodiments, a signal is acquired from at least one sensor, such as at least one EMG sensor, at block 802. In some embodiments, the acquired signal is stored in a memory of the evaluation device, such as Figure 2A and Figure 3 Memory 216 or memory 314 is shown.

[0322] According to some exemplary embodiments, at block 804, the fixed judder emphasis filter and the fixed judder attenuation filter are retried from memory.

[0323] According to some exemplary embodiments, at block 806, a tremor emphasis filter is applied to the acquired sensor signal. In some embodiments, at block 808, a tremor signal feature is calculated based on the filtered emphasized signal.

[0324] According to some exemplary embodiments, at block 810, a chatter attenuation filter is applied to the acquired sensor signal. In some embodiments, at block 812, a non-chatter signal feature is calculated based on the filtered attenuated signal.

[0325] According to some exemplary embodiments, for example Figure 8B As shown, adaptive filtering is applied to the recorded signals to separate tremor-related signals from non-tremor-related signals.

[0326] According to some exemplary embodiments, the sensor signal acquired at block 802 includes an EMG signal.

[0327] According to some exemplary embodiments, an EMG envelope is detected in the acquired EMG signal. In some embodiments, the EMG envelope is detected by applying a Hilbert transform algorithm to the acquired EMG signal.

[0328] According to some exemplary embodiments, at block 816 , a tremor frequency, such as a fundamental tremor frequency, is calculated from the detected envelope.

[0329] According to some exemplary embodiments, at block 818 , an emphasis filter and / or an attenuation filter is calculated. In some embodiments, one or both filters are calculated based on the chatter frequency calculated at block 816 .

[0330] According to some exemplary embodiments, at block 820, the calculated tremor emphasis filter is applied to the acquired EMG signal. In some embodiments, at block 822, a tremor signal feature is calculated. In some embodiments, the tremor signal feature is calculated based on the emphasized tremor signal filtered at block 820.

[0331] According to some exemplary embodiments, at block 824, the calculated tremor attenuation filter is applied to the acquired EMG signal. In some embodiments, at block 826, a non-tremor signal feature is calculated. In some embodiments, the non-tremor signal feature is calculated based on the attenuated tremor signal filtered at block 824.

[0332] According to some exemplary embodiments, for example Figure 8C As shown, prior to feature calculation of, for example, an EMG signal, an independent component analysis (ICA) is applied to the acquired sensor signal.

[0333] According to some exemplary embodiments, at block 828, an independent component analysis is applied to the acquired EMG signal.

[0334] According to some exemplary embodiments, at block 830 , a tremor component is identified in the results of the ICA analysis based on known characteristics of the output ICA components, such as amplitude, fundamental frequency, harmonic distortion, entropy, kurtosis.

[0335] According to some exemplary embodiments, an output ICA component is identified whose characteristics are most similar to typical characteristics of the symptom-related component. In some embodiments, the identified tremor component is stored separately from the identified non-tremor components.

[0336] According to some exemplary embodiments, at block 832, a tremor component feature is calculated. In some embodiments, the tremor component feature is calculated based on the identified tremor component.

[0337] According to some exemplary embodiments, at block 834, non-tremor component features are calculated. In some embodiments, the tremor component features are calculated based on the identified non-tremor components.

[0338] Example Tremor Analysis

[0339] According to some exemplary embodiments, processing of signals related to tremor is performed in different processing methods. In some embodiments, EMG signals are received and processed to detect tremor.

[0340] In some embodiments, the signal processing includes an "on demand" signal processing, such as signal processing initiated in response to an indication, such as a signal from a control circuit or a user. In some embodiments, the "on demand" signal processing is used to quantify tremor in EMG signals recorded at a specific location, such as one or more of the left face, right face, left upper limb, left lower limb, right upper limb, right lower limb.

[0341] According to some exemplary embodiments, the processing method is also used for the acquisition and signal processing of other symptoms and / or side effects. In some embodiments, when recording signals for detecting at least one side effect and / or at least one disease symptom, the patient is asked to perform different tasks or rest. In some embodiments, at each stimulation level (or other treatment level), the patient is checked for the presence of various symptoms and / or side effects, so that the signal that should be processed to obtain an index of a specific symptom (tremor in this embodiment) does not arrive continuously, but the processing should be performed "on demand". In some embodiments, an instruction from a user or a control circuit starts recording a signal from at least one sensor to evaluate at least one side effect and / or at least disease symptom. Alternatively, an instruction from a user or a control circuit marks a window in a previously recorded signal from at least one sensor, for example using the signal within the marked window to evaluate at least one side effect and / or at least one disease symptom.

[0342] According to some exemplary embodiments, the system waits for an indication, such as a flag or trigger, to signal that the acquired EMG signal is to be used as input to a particular signal processing process, such as tremor signal processing. In some embodiments, the indication is automatically generated by the system. In some embodiments, when the system detects that the patient is at rest, the system generates an indication that the currently recorded EMG signal is to be used for one or more of tremor, intercapsular recruitment, and EMG-based rigidity. In some embodiments, when the system detects that the patient moves his eyeballs in a stereotyped manner, for example, moving the eyeballs significantly to one side and then significantly to the other side, the system indicates that the recorded signal is to be used for gaze disorder processing.

[0343] According to some exemplary embodiments, processing of inertial measurement unit (IMU) signals for rigidity analysis is automatically triggered, for example by identifying stereotyped, repetitive, large movements about the axis of the elbow. In some embodiments, processing of microphone signals for identifying dysarthria is triggered, for example by identifying stereotyped articulation patterns uttered by a patient for testing dysarthria. Alternatively, a user initiates processing manually by pressing a button or another input device to issue the desired trigger signal, or verbally, by speaking the name of the symptom being tested, the utterance being picked up by the microphone and automatically recognized by speech processing circuitry in the system.

[0344] Reference now Fig.9A , Fig.9A A general on-demand process for initiating analysis to detect tremor is depicted according to some exemplary embodiments of the present invention.

[0345] According to some exemplary embodiments, at block 902 , the evaluation system remains in a standby mode until an indication regarding a chatter signal measurement is received.

[0346] According to some exemplary embodiments, at block 904, at least one EMG signal is obtained when the indication is received, for example according to a new stimulation level.

[0347] According to some exemplary embodiments, at block 906, the obtained signal is filtered using a high pass filter.

[0348] According to some exemplary embodiments, at block 908, a power spectral density (PSD) is calculated.

[0349] According to some exemplary embodiments, at block 910 , the power spectral density result is normalized, for example, divided by a maximum value in the power spectral density.

[0350] According to some exemplary embodiments, the results of the power spectral density or the results following the standardization are displayed in the user interface. In some embodiments, information is presented in a frequency range according to the detected side effect or disease symptom, for example, the displayed frequency range is in the range of 2 to 8 Hz (for Parkinson's tremor). In some embodiments, this allows the user to focus on 3 to 7 Hz, which is the frequency band of Parkinson's tremor. In some embodiments, for essential tremor (ET) patients, the displayed frequency range is in the range of 2 to 14 Hz, for example to allow viewing of the ET tremor range of 4 to 12 Hz. In some embodiments, these ranges are recommended for patients with typical tremors, however the user interface is configured to modify the displayed range to better suit a particular patient or user / clinician's preferences.

[0351] Reference now Fig. 9B , which depicts the use of the overall maximum PSD value according to some exemplary embodiments of the present invention.

[0352] According to some exemplary embodiments, tremor is quantified in response to an indication based on electromyographic recordings from one or more specific myoelectric points, such as left face, right face, left upper limb, left lower limb, right upper limb and / or right lower limb.

[0353] According to some exemplary embodiments, at block 912, for example, for a particular myopotential point, the overall maximum power spectral density value SmaxG is stored in a memory (initialized to be equal to 0). In some embodiments, each myopotential point has its own overall maximum value, and analysis is performed separately for each myopotential point.

[0354] According to some exemplary embodiments, at block 914, the evaluation system is in a standby state, awaiting instructions from the system or a user.

[0355] According to some exemplary embodiments, upon receiving an indication, such as an indication to process another input electromyogram signal for tremor quantification, an electromyogram signal is obtained at block 916. In some embodiments, the electromyogram signal is obtained at a new stimulation level.

[0356] According to some exemplary embodiments, at block 918, the obtained signal is filtered using a high pass filter.

[0357] According to some exemplary embodiments, at block 920, a power spectral density value S1 is calculated.

[0358] According to some exemplary embodiments, at block 922 , a maximum power spectral density value Smax1 in the current signal is calculated.

[0359] According to some exemplary embodiments, at block 924, the maximum value Smax1 of the power spectral density of the current stimulation level is compared with SmaxG.

[0360] According to some exemplary embodiments, if SmaxG is larger, then S1 is normalized relative to SmaxG at block 926 .

[0361] According to some exemplary embodiments, if Smax1 is larger, then S1 is normalized relative to Smax1 at block 928 .

[0362] According to some exemplary embodiments, at block 930, all previously obtained power spectral densities are renormalized to Smax1, and Smax1 is defined as the new SmaxG at block 932. Alternatively, to renormalize a previously normalized power spectral density Sx to a new level Smax1, it is sufficient to multiply Sx by SmaxG and then divide by Smax1.

[0363] Reference now Fig. 9C , which illustrates the use of an integral value, which is a maximum prominence value calculated for a peak in a PSD signal during processing of the signal to detect tremor, according to some exemplary embodiments of the present invention. Fig. 9C In the process described in , after calculating the peak prominence, only the maximum peak prominence value is kept, and the values ​​of the PSD at all other frequencies are replaced by zero.

[0364] According to some exemplary embodiments, at block 934, for example, for a particular myopotential point, the overall maximum protrusion value PmaxG is stored in a memory (initialized to be equal to 0). In some embodiments, each myopotential point has its own overall maximum value, and each myopotential point is analyzed separately.

[0365] According to some exemplary embodiments, at block 936, the evaluation system is in a standby state, awaiting instructions from the system or a user.

[0366] According to some exemplary embodiments, upon receiving an indication, for example, a flag to process another input electromyogram signal for tremor quantification, an EMG signal is acquired at block 938. In some embodiments, the electromyogram signal is acquired at a new stimulation level.

[0367] According to some exemplary embodiments, at block 940, the obtained signal is filtered using a high pass filter.

[0368] According to some exemplary embodiments, at block 942, a power spectral density is calculated.

[0369] According to some exemplary embodiments, at block 944, peaks in the PSD for the current stimulation level are detected and prominence is calculated for all peaks.

[0370] According to some exemplary embodiments, at block 946 , a maximum protrusion value Pmax1 and a frequency Fmax1 at which Pmax1 occurs are detected.

[0371] According to some exemplary embodiments, at block 948, the PSD value is replaced with zeros everywhere except for Fmax1, where the value is replaced with Pmax1. In some embodiments, this step is performed to clarify the display of the signal, for example by keeping only the most prominent peaks and removing other components that may be distracting. Alternatively, at locations where peaks are detected, the PSD value will be replaced with the prominent peak, and at frequencies where no peaks are detected, the PSD value will be replaced with zeros.

[0372] According to some exemplary embodiments, at block 950 , Pmax1 is compared to PmaxG.

[0373] According to some exemplary embodiments, if PmaxG is larger, then at block 952 , S1 is normalized relative to PmaxG.

[0374] According to some exemplary embodiments, if Pmax1 is greater, then S1 is normalized relative to Pmax1 at block 954. Additionally, all previously obtained PSDs are renormalized to Pmax1 at block 956, and Pmax1 is defined as the new PmaxG at block 958. In some embodiments, to renormalize a previously normalized PSD value Sx to the new Pmax1 level, Sx is multiplied by PmaxG and then divided by Pmax1.

[0375] FIG. 9D to FIG. 9G The results of quantitative experiments comparing three analytical methods are described, such as 9A to 9C shown.

[0376] Table 1 summarizes some parameters of the recordings performed in the first experiment:

[0377] Record date Brain side Fraction Notes Stimulation Ampere 30 / 01 / 2019 Left 2 Undefined 1- 30 / 01 / 2019 Left Undefined Persistent tremor 250 30 / 01 / 2019 Left 0 The tremor stops 500 30 / 01 / 2019 Left 0 The tremor stops 1000

[0378] Reference now Fig.9D , Fig.9D Depicted are examples of analyzing multiple EMG signals according to some exemplary embodiments of the present invention and displaying outputs highlighting tremor levels in various ways.

[0379] According to some exemplary embodiments, the three columns correspond to three sites, Site 1, Site 2, and Site 3, respectively, which are EMG recording sites, or locations on the subject's body where EMG electrodes are positioned, such as the face, arms, and legs. In some embodiments, each of the three columns corresponds to a previously 9A to 9C One of the three exemplary tremor analysis methods described in . In some embodiments, the first column of the left column displays PSD values, where each calculated PSD value is normalized relative to its own maximum value, e.g. Fig.9A The middle column, column 2, shows the PSD, where each PSD value for each site is normalized to the maximum PSD value calculated for each site, e.g. Fig. 9B The third column on the right shows the PSD protrusion values, where only the maximum protrusion is kept for each calculated PSD value, and each protrusion value is normalized to the maximum PSD protrusion value calculated for each site, e.g. Fig. 9C shown.

[0380] According to some exemplary embodiments, the displayed frequency range is between f1 and fh, where in the case of a typical Parkinson's patient, f1 can be approximately 2 Hz and fh can be approximately 8 Hz, and for a typical essential tremor patient, f1 can be approximately 4 Hz and fh can be approximately 12 Hz. In this embodiment, 4 increasing stimulation levels are depicted at each site, from s1 to s4 (only s1 and s4 are shown for clarity). For example, s1 can be zero and s4 can be 2 mA. In this embodiment, there is a significant tremor in site 1, which decreases as the stimulation level increases. This can be well observed in the top row, middle column, and right column, where there is a significant peak in the PSD value, whose height and the area below it decrease as the stimulation increases. No significant tremor was found at sites 2 and 3.

[0381] Fig.9E The results of three processing methods of the signals received from the facial electrodes are summarized. Fig.9F The results of three processing methods for the signals received from the arm electrodes are summarized. Figure 9G The results of three processing methods of the signals received from the leg electrodes are summarized.

[0382] Figure 9H shows an example of a high-pass filter with a 1 Hz cutoff frequency, as described in this paragraph.

[0383] Exemplary Sensing and Quantification Strategies

[0384] According to some exemplary embodiments, the assessment system includes at least one optical sensor, such as a camera. In some embodiments, the camera is used to quantify one or more of the side effects of tremor, dyskinesia, postural instability, gait disturbance, stiffness, or muscle recruitment. Optionally, the camera is also used to detect gaze abnormalities induced by the treatment.

[0385] According to some exemplary embodiments, video-based attribute quantization is achieved through at least one of two strategies. In some embodiments, the first strategy is segmentation of the video sequence to identify substructures in the image corresponding to one or more of the limbs, head, torso, or facial structures, and processing the video stream per substructure to compute various features of the substructure's motion. In some embodiments, the second strategy is to process the image as a whole, or perhaps define a structure as a foreground, and process dynamic pixels associated with the foreground structure, such as computing various features of the structure's motion.

[0386] According to some exemplary embodiments, segmentation of a single foreground structure or several substructures is based on edge detection or texture recognition, or on other methods such as multivariate clustering considering edge features, color, texture, spatial frequency components or other features of a group of pixels in an image or video sequence.

[0387] According to some exemplary embodiments, for any of the above strategies, one or more of the following exemplary features are calculated for a single or multiple structures: the physical extent of the motion (i.e., how far the structure moves), the variability of the range of motion quantified as variance or standard deviation, the coefficient of variation of the range of motion, or another measure of variability. Optionally, additional features are related to the occurrence of motion, i.e., whether the motion is continuous or intermittent, and if intermittent, the average interval between motions, the median interval between motions, the variability of the intervals, and similar features may be applied.

[0388] According to some exemplary embodiments, in the time-frequency domain, the features include power in various frequency bands, such as 2 to 4 Hz, 4 to 7 Hz, 8 to 12 Hz, etc. or any intermediate, smaller or larger frequency range. Additionally or alternatively, the time-frequency domain includes the magnitude of the peaks in the power spectral density (PSD), and optionally the corresponding frequencies of the peaks in the PSD. In some embodiments, the total harmonic distortion (THD) associated with a particular fundamental frequency is calculated as:

[0389]

[0390] It measures the degree to which rhythmic movement deviates from sinusoidal motion.

[0391] According to some exemplary embodiments, when the first strategy of substructure segmentation is adopted, paired calculations are performed between pairs of substructures, such as correlation or cross-correlation, phase delay and mutual interference calculations. In some embodiments, high-order calculations can also be performed to quantify the relationship between the motions of three or more substructures.

[0392] According to some exemplary embodiments, tremor detection is based on detecting rhythmic movements in limbs, head, or facial muscles. In some embodiments, highly rhythmic movements are identified by large peaks in the frequency domain, for example in the range of 3 to 7 Hz or any intermediate, smaller or larger frequency range. In some embodiments, large peaks at fundamental vibration frequency ft in the range of 3 to 7 Hz are accompanied by peaks at harmonic frequencies that are products of m×ft, m=2, 3, etc. In some embodiments, in the case of tremor, high cross-correlation and cross-correlation values ​​can be expected because rhythmic movements that often occur in more than one limb often have the same fundamental frequency and may appear and disappear synchronously in various body parts.

[0393] According to some exemplary embodiments, when the patient is instructed to rest and not perform voluntary movements, tremors are identified and quantified, further highlighting involuntary movements associated with tremors. The same is true for quantification of movement disorders, motor side effects, and gaze abnormalities.

[0394] According to some exemplary embodiments, quantification of movement disorders employs similar two strategies described above for processing video sequences of pixels in a single foreground structure or multiple substructures. In some embodiments, for example, in the case of movement disorders, cross-correlations and / or cross-interferences are expected to be lower than in patients exhibiting tremors. Additionally, if the movement is completely devoid of any observable values, the movement is typically not rhythmic and the frequency domain peaks are expected to be lower. In some embodiments, the maximum range of motion is expected to be greater than the maximum range of motion found during tremors.

[0395] According to some exemplary embodiments, identification and quantification of motor side effects requires sensitivity to contractions of muscles in the face, arms, and / or legs. In some embodiments, contractions will be visible as facial muscles are pulled and induce movement near the corners of the mouth or eyes. These are typically isolated phenomena, in the sense that a treatment-induced muscle contraction will be seen in one part of the body without a similar contraction in another part of the body. This spatial limitation of phenomena makes them more difficult to detect because global features calculated from the entire image or structure will "average out" the local effects of muscle contraction. In some embodiments, quantification of muscle contraction side effects requires calculating features from smaller substructures in the video sequence.

[0396] According to some exemplary embodiments, postural stability is quantified while the subject is standing and / or walking. In some embodiments, gait features are visible while the subject is walking. In some embodiments, quantification of gait and / or postural stability requires different setup and camera configurations than, for example, those required to detect and quantify localized manifestations of treatment-induced recruitment of motion. While the latter setup and configuration is intended to be able to detect subtle changes in smaller areas of the face, the former aims to capture images of the entire body or large areas of the body, which may be static or, optionally, in motion over several walking cycles, and thus the required setup and configurations may be substantially different. Therefore, in some embodiments, in order to achieve quantification of the side effects of treatment-induced recruitment of motion and one of the symptoms of postural instability and gating disorder, at least one additional camera is required during the preparation process, as well as at least one additional setup stage, or the camera settings must be updated as needed during the recording period.

[0397] According to some exemplary embodiments, quantifying the degree of rigidity through video sequence analysis requires that the patient's limb cannot be still. In some embodiments, the limb is moved by a second person or the subject themselves, and the analysis focuses on quantifying how easy passive movement is, or how the limb continues to move passively after the manipulation ends.

[0398] According to some exemplary embodiments, in order to optimize the performance of the camera, a specific background is used. In some embodiments, the clinician or subject is instructed to select a location in the house, whether in a clinic, in a house, or elsewhere, in which the background best meets the predetermined requirements. In addition, in this case, the system provides an indication of the quality of the degree of compliance of the background with the requirements through a score, such as a score of 1-10 or a binary compliance / non-compliance indication. Alternatively, the clinician or subject uses a specific background sheet or cloth. Optionally, the sheet or cloth does not have any lines or texture changes. Alternatively, the sheet or cloth has a background with clean lines and texture changes, and a foreground of lines arranged at regular intervals or certain predefined dividers or selected patterns.

[0399] According to some exemplary embodiments, the clinician or the subject, or a person assisting the subject in a home environment, is instructed to position the cloth at a specific distance behind the subject when capturing the video. Additionally or alternatively, instructions are provided regarding a specific angle at which the background is located relative to the subject and the camera. Such background can help calibrate the video processing algorithm by assessing the distance to the subject or the angle to the subject. Alternatively or additionally, the background improves the video quantization performance by enhancing the contrast between the subject and the image background.

[0400] Example Condition Assessment Using Electromyography (EMG)

[0401] According to some exemplary embodiments, the EMG recordings are used to assess the subject's condition before, during and after a brain stimulation treatment, such as DBS. In some embodiments, the EMG recordings are used to quantify at least one symptom of a neurological disease and / or at least one side effect of a brain stimulation treatment.

[0402] According to some exemplary embodiments, EMG electrodes are applied to predetermined muscles of a patient, such as a patient with a neurological disease. In some embodiments, the electrodes are then connected to an evaluation system or an evaluation device.

[0403] According to some exemplary embodiments, the signal is recorded during baseline conditions, such as when the patient is resting or when treatment is stopped. Alternatively, the signal is recorded for a selected period of time, which is then referred to as the reference signal.

[0404] According to some exemplary embodiments, at least one parameter associated with the treatment is changed, such as stimulation amplitude, stimulation frequency, stimulation duration, the number of stimulation pulses in a pulse train, the number of sequences and / or the duration of each sequence. In some embodiments, the at least one parameter includes the position of at least one stimulation electrode along the lead, the number of stimulation electrodes, the insertion depth of the lead and / or the position of the at least one electrode in the brain.

[0405] According to some exemplary embodiments, the signals are recorded while the patient is at rest. In some embodiments, the patient is then instructed to perform a task, and additional signals are recorded during and after performance of the task.

[0406] According to some exemplary embodiments, signals, such as baseline signals, signals recorded in stillness, and recorded signals, and task-related signals. In some embodiments, the signals are preprocessed before feature calculation. In some embodiments, features are calculated from preprocessed signals.

[0407] According to some exemplary embodiments, an index of one or more symptoms, signs or side effects is calculated. In some embodiments, the index is calculated as at least one linear combination or at least one non-linear combination of the calculated features.

[0408] Now refer to Fig. 10A and Fig. 10B , which depicts locations for placing EMG electrode pairs according to some exemplary embodiments of the present invention.

[0409] According to some exemplary embodiments, one or more EMG electrode pairs are placed on the patient's face 1002, at least one hand 1004, and at least one leg 1006. In some embodiments, at least one EMG electrode is placed at a location 1008 on the face, such as for recording signals from the orbicularis oculi muscle. In some embodiments, at least one EMG electrode is placed at a location 1010 on the face, such as for recording signals from a mixture of two or more of the zygomatic, masseter, buccinator, and gluteal muscles.

[0410] According to some exemplary embodiments, at least one EMG electrode is placed at location 1012 on hand 1004, for example, to record signals from the extensor carpi radialis and / or the flexor carpi radialis.

[0411] According to some exemplary embodiments, for example Fig. 10B As shown, at least one EMG electrode is positioned at a location 1016 on the hand, for example, to record a potential difference between the opponens pollicis and a mixed site of the opponens digiti minimi and the flexor digiti minimi brevis.

[0412] According to some exemplary embodiments, for example FIG. 11A to FIG. 11F , and as Fig. 8A As shown, a time domain and / or time-frequency representation is generated from the original recorded EMG signal, and a tremor-emphasis filter and a tremor-attenuation filter are then used to highlight tremor- and non-tremor-related signals, respectively.

[0413] According to some exemplary embodiments, for example Fig.12 As shown, envelope detection is performed on the wrist EMG signal, followed by PSD estimation, for example, to identify the envelope peak frequency.

[0414] Example Rigidity Assessment

[0415] According to some exemplary embodiments, the assessment system is configured to assess stiffness by means of a sensor, which is a stiffness measuring device designed to quantify mechanical properties of a limb rotating about a joint, such as an arm, wrist, or ankle, as a representative of clinical symptoms of muscle stiffness. In some embodiments, the device, such as the device described in "A Portable System for Quantitative Assessment of Parkinson's Stiffness" by Houde Dai, Bernward Otten, Jan Hinnerk Mehrkens, LTD'Angelo, 35th Annual IEEE EMBS International Conference, 2013, and "Quantification of the UPDRS Stiffness Scale", Susan K. Patrick, Allen A. Denington, Michel JAGauthier, Deborah M. Gillard, and Arthur Prochazka, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 9, No. 1, March 2001, utilizes Newton's second law of motion in angular applications: T = Iα, where T is the torque applied to the limb, I is the moment of inertia, and α is the angular acceleration. In some embodiments, the rotational resistance represented by I in the equation depends on the passive mechanical properties of the limb and the reactive mechanical properties of the muscles, which are affected by the presence of rigidity symptoms.

[0416] According to some exemplary embodiments, a stiffness measurement device including a plurality of sensors is shaped and sized to be attached to a measured limb. In some embodiments, the stiffness measurement device is shaped as a cuff that is positioned over the arm and is elastic and fits snugly to the limb, or has some specific tightening / loosening features, such as hook and loop fasteners. In some embodiments, at least some of the sensors are sensitive to changes in position, such as accelerometers, gyroscopes, and magnetometers.

[0417] According to some exemplary embodiments, these sensors are located in a single package called an inertial measurement unit. In some embodiments, because the movement of a limb in a real environment occurs in three axes, each property (acceleration, angular velocity, or magnetic field) is measured in three axes. In some embodiments, the purpose of utilizing these inertial measurement unit (IMU) sensors (whether or not packaged in an IMU) is to accurately record the location of a position on the limb despite the inherent errors in each sensor. In some embodiments, the combination of an accelerometer and a gyroscope is sufficient to obtain a reasonably accurate position. Alternatively, a magnetic sensor reading that is sensitive to horizontal movement is added to an accelerometer reading that is most sensitive to gravity in the vertical direction.

[0418] According to some exemplary embodiments, the limb is moved controllably, automatically, or semi-automatically, such as by a mechanical device in which the applied force is intrinsically measured. Alternatively, the limb is moved by a second person or a device that does not directly control the force (such as a continuous passive motion device), and the force is then measured by one or two dynamometers attached to the rigidity measurement device, which are sensitive to the force applied by the second person or machine. In some embodiments, in order to convert the force measurement to a torque, the distance from the point of force application to the joint must be measured or estimated, such as T=Fl, where F is the net applied force and l is the moment arm.

[0419] According to some exemplary embodiments, the mechanical measurements are then used to calculate the mechanical parameters of the limb by the equation T=c|ω|+d|θ|+e, where ω and θ are the angular velocity and limb angle, respectively. Calculated from the IMU sensor readings, c and d are the elastic stiffness and viscosity of the limb, and e is a constant error. In some embodiments, c, d, and e are scalar parameters, while T, ω, and θ are continuous variables calculated from the readings. Therefore, the elastic stiffness and viscosity can be estimated from a set of readings by any fitting tool, such as linear regression. In some embodiments, another related indicator is mechanical impedance, defined as Z=c+d2πf, where f is the repetition frequency of the limb movement. In some embodiments, the parameters c, d, and Z are related to the presence and severity of rigidity to varying degrees.

[0420] According to some exemplary embodiments, the output of the rigidity measurement module is used alone or in combination with EMG measurements, as described herein, for example, to obtain a more robust quantification of rigidity levels.

[0421] According to some exemplary embodiments, a process of obtaining a rigidity measurement comprises the following steps:

[0422] a. Attach the stiffness measurement device to the patient's arm according to the specific instructions (e.g., the arrow on the device indicates pointing toward or away from the elbow).

[0423] b. Optionally, measure, estimate or otherwise obtain an estimate of the length I between the center of the stiffness measurement device and the patient's elbow, or more precisely, obtain an estimate of the length I between the center of the stiffness measurement device and the patient's elbow fulcrum. The length I is the moment arm length, which is used to convert the force measurement into the moment measurement. The estimation may not necessarily require performing the measurement, for example, the length can be estimated from other attributes of the patient (e.g., height, weight, age, etc.).

[0424] c. Optionally, starting from a tested arm rest and horizontal posture (approximately 1-2 seconds), the operator does not need to apply any force (or very little force) to hold the device. This allows an initial period in the recording at a known position, thereby improving the estimate of the initial orientation (based on the accelerometer's measurement of the effect of gravity) and the location of periodic operations in the signal.

[0425] d. Optionally, hold the subject's elbow with one hand - on the side being measured. Alternatively, immobilize the elbow. e. Hold the patient's arm being measured with a second hand through the rigidifying device in the position marked on the device to ensure that the applied force is measured by the force sensor. Alternatively, a mechanical device, such as a lever or a robotic arm, can be used instead of a human to hold the subject's hand.

[0426] f. Repeat vertical or horizontal flexion and extension of the patient's arm around the elbow axis. In some embodiments, a force is applied that causes rotation around the elbow joint or optionally around the wrist joint. In some embodiments, flexion / extension and repetition are optional.

[0427] g. Optionally, complete the process by returning to a stationary horizontal position (approximately 1 to 2 seconds).

[0428] Calculation procedure

[0429] h. Determine the time interval between the initial rest and the repetition operation. You can choose to do the following:

[0430] i. Generate a 1D signal from the three gyroscope signals, one gyroscope signal per axis. This can be achieved by:

[0431] 1. The total energy of the three signals each time sampled

[0432] 2. Select the signal from the axis with the largest variation during manipulation. This can be done by calculating the interquartile range (IQR) of the signal for each axis and selecting the axis with the highest IQR. IQR is more robust to noise and outliers than a simple range (max(x) - min(x)).

[0433] ii. Convert the gyroscope signal to a representation that emphasizes the total motion energy. Example:

[0434] 1. Smooth the gyro signal with a moving window or low-pass filter

[0435] 2. Take the square (x 2 )

[0436] iii. Process the results to locate the operation

[0437] 1. Establish baseline mean and standard deviation (STD), or median and MAD.

[0438] 2. Starting from the beginning of the signal (time = 0), find the time point in the signal where the deviation from the mean + standard deviation exceeds the threshold.

[0439] 3. Check a minimum number of consecutive threshold crossing samples to ensure that the threshold crossing represents a change from a “resting” state to a “manipulated” state and is not the result of random noise or artifacts.

[0440] 4. Perform checks similar to steps (2) and (3) starting from the end of the signal and working backward to determine the transition from the manipulated state back to the "stationary" state.

[0441] In some embodiments, if the force is applied by a mechanical device, such determination includes reading an output of the device as the device applies the force to the patient. As described above, the repetitive operation is optional.

[0442] i. In some embodiments, the initial orientation of the device is determined during detection of a "stationary state". This is accomplished by analyzing the accelerometer signals, which are primarily affected by gravity when stationary. The static offset values ​​measured by the 3 accelerometer axes at stationary are the 3 components of gravity (x, y, z), and by knowing the orientation of the device attached to the arm (see 1.a.), the orientation in three dimensions can be fully understood.

[0443] j. Apply the orientation detection algorithm cited by Dai et al. to detect the angle between the arm and the horizon during the operation. The baseline direction is used in the previous step to optionally correct the calculated angle so that it corresponds to the angle between the elbow and the horizontal line. In some embodiments, the orientation detection algorithm needs to be applied when a human moves the patient's arm and there is no other angle sensor (such as a goniometer). In some embodiments, if the arm movement is performed by a mechanical device, the orientation detection algorithm is not required.

[0444] k. Using the calculated angle θ, its time derivative ω and the moment T=F1, perform the above fitting or regression operation to extract the elastic modulus and viscosity. In some embodiments, the angle θ can be directly measured without calculation.

[0445] Example EMG rigidity analysis

[0446] According to some exemplary embodiments, the degree of rigidity is estimated from an EMG recording of a patient at rest. In some embodiments, before performing the rigidity analysis, the rigidity-related signal is separated from the tremor-related signal.

[0447] Reference now Fig.13A and Fig. 13B , shows the results of EMG rigidity analysis.

[0448] In experiments and in some embodiments, tremor analysis is performed by first decimating the acquired signal, for example, to 440 Hz. After decimation, the signal is passed through a bandpass filter, for example, a 2 to 13 Hz bandpass filter. In experiments and in some embodiments, a Gaussian-kernel moving window RMS is calculated. The analysis results are displayed as a bar graph, for example, by displaying the mean and / or median of the RMS during stimulation.

[0449] In experiments and in some embodiments, the rigidity analysis was performed by passing the acquired signal through an LPF filter, such as an LPF filter with a cutoff frequency of 2000 Hz, followed by an HPF filter, such as an HPF filter with a cutoff frequency of 20 Hz. In experiments and in some embodiments, a Gaussian-kernel moving window RMS was calculated, which is a method of calculating the average RMS around a specific time point. The results of the analysis were displayed, for example, as a bar graph, by displaying the mean and / or median of the RMS during stimulation. In experiments, it was found that the electromyographic signals recorded when the patient was at rest, after filtering out the effects of tremor, were associated with clinical symptoms of rigidity. In some embodiments, and in experiments, it was found by expert clinical assessment that a reduction in power in the frequency band of 20 to 2000 Hz, quantified as described above, occurred at the same therapeutic level as a reduction in rigidity.

[0450] exist Fig.13A and Fig. 13B In the figure, the first column represents the moving window RMS of the rigidity-processed signal + the signal. The second column represents the moving window RMS of the tremor-processed signal + the signal. The third column represents the moving window RMS of the rigidity and tremor-processed signals. The fourth column represents the time-frequency representation of the original EMG signal. The fifth column represents the rigidity index for each stimulus level, which is calculated by taking the mean or median of the rigidity-processed signal at each stimulus level. The sixth column represents the tremor index for each stimulus level, which is calculated by taking the mean or median of the rigidity-processed signal at each stimulus level.

[0451] exist Fig.13A and Fig. 13B In the figure, columns 1 to 3, the x scale is time [seconds], and the y scale is microvolts; column 4, the x scale is time in seconds, and the y scale is frequency in [Hz] (logarithmic scale); columns 5 and 6 – the x scale is the magnitude of the rigidity or tremor index, and the y scale is the applied stimulation current in milliamperes.

[0452] exist Fig.13A and Fig. 13BIn , the first row represents the EMG signals measured near the subject's eyeballs; the second row represents the EMG signals measured near the subject's mouth; the third row represents the EMG signals measured from the subject's arms; the fourth row represents the EMG signals measured from the subject's wrists; and the fifth row represents the EMG signals measured from the subject's legs. Fig. 13B , the arrows in the 2nd column, 3rd row, and 5th row point to the stimulus levels at which the stiffness index of the arms and legs decreases, respectively.

[0453] exist Fig.13A , in column 1, two translucent rectangles, such as translucent rectangle 1310, depict time intervals during which the level of treatment delivered was clinically found to reduce stiffness in patients. The arrows in rows 3 and 5 of column 1 point to time points after which the EMG signal processed for stiffness analysis as described above will be significantly reduced. In row 5, the arrows and translucent rectangles coincide, indicating that the changes in the signal are timely consistent with the clinical assessment. In row 3, the arrows and translucent rectangles do not completely coincide, but this may be due to the experimental method, that is, the clinical assessment occurred within one stimulation cycle and the EMG-based assessment occurred in a second cycle a few minutes later. Therefore, the overall stimulation mechanism is not exactly the same and some differences may occur. Therefore, the results shown in row 3 may also be an example of a correlation between a reduction in clinical assessment of stiffness and a stiffness-treated signal.

[0454] Example Speech and Articulation Dysfunction Assessment

[0455] According to some exemplary embodiments, speech and / or articulation disorder assessment is performed based on signals recorded by at least one audio sensor. In some embodiments, the audio sensor captures the subject's speech intelligibility and, for example, processes the signal to quantify at least one of two attributes: the amplitude of the speech and the intelligibility of the speech.

[0456] According to some exemplary embodiments, to quantify dysarthria, articulation at a particular treatment level or using a selected set of treatment parameter values ​​is compared to prior articulation scores for the same patient at past time points or to articulation scores for other subject populations. In some cases, such as when adjusting DBS or pump treatment levels intraoperatively or postoperatively, articulation at a particular treatment level is compared to the same patient's articulation in the absence of treatment, or to articulation at a baseline treatment level, optionally within the time frame of a tuning session (up to about 1 hour, or up to about 20 minutes, or any shorter or longer period of time). In some embodiments, the processing required to perform this comparison is based on general signal processing methods, such as filtering, envelope detection, and spectral estimation, and optionally uses methods from the more focused areas of speech recognition and speaker recognition.

[0457] According to some exemplary embodiments, speech recognition technology is intended to clearly translate the acquired pronunciation of one or more languages ​​into specific words, for example, using the method described in "Speech Recognition with Deep Recurrent Neural Networks" published by Graves et al. at the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. In some embodiments, the process of such technology includes acquiring the pronunciation sound (sensing the physical sound waves and digitizing them), preprocessing them by optionally filtering, and then calculating a set of representation features. In some embodiments, the representation feature combination includes a time-frequency representation of the sound (optionally through Mel Frequency Cepstrum (MFC), but short-time Fourier transform, wavelet transform and other methods can be applied), which is then optionally fed to a machine learning classifier that matches the maximum likelihood letters with signal time bins. According to some exemplary embodiments, the machine learning classifier is trained on a data set including multiple digitized utterances and their text translations. Among them, the most commonly used classifiers are deep recurrent neural networks (RNNs), hidden Markov models (HMMs) and combinations of HMMs with various neural networks.

[0458] According to some exemplary embodiments, speaker recognition technology is intended to identify the identity of a speaker based on or without a specific spoken text. In some embodiments, the general process of acquisition, preprocessing, feature extraction and classification is similar, and the difference from speech recognition is that the classifier is trained to minimize the error of correctly identifying the speaker, and the features are selected to minimize such classification errors. According to some exemplary embodiments, MFC coefficients are used as features for speaker recognition, as well as mean minus cepstrum, and the first and second derivatives of these features (called variables). Optionally, other features used for speech recognition and speaker recognition include one or more of frequency domain linear prediction (FDLP), mean Hilbert envelope coefficients (MHECs) and power normalized cepstrum coefficients (PNCCs). In some embodiments, the classifier is based on a non-parametric model, such as dynamic time warping (DTW) or K-nearest neighbor algorithm, or based on a parametric model, such as vector quantization, Gaussian mixture model, HMMs and support vector machines (SVMs).

[0459] According to some exemplary embodiments, in a particular branch of speaker verification, the goal is to determine whether the likelihood that the speaker is the particular person the system was trained on is significantly greater than the likelihood that the speaker is any other speaker in the population. In some embodiments, in this case, a similarity index is calculated from a set of features to represent the similarity between the test utterance and the sounds in the training database, and compared to a threshold representing how "significance" is defined in the particular system.

[0460] According to some exemplary embodiments, in the context of identifying disease-related or treatment-induced changes in pronunciation, identifying the speaker or spoken text is not the goal, but there are several ways to utilize these techniques. One approach is to compare the speech recognition success rate of the same spoken text in any treatment configuration and under baseline conditions. In some embodiments, an increase in recognition success rate is associated with a reduction in worsening voice symptoms, while a decrease in recognition success rate indicates a side effect of dysarthria. In some embodiments, dysarthria is identified by using speaker verification techniques when the vocalizations of a patient receiving DBS treatment are not identified as belonging to the same patient recorded at the baseline treatment level.

[0461] According to some exemplary embodiments, a second method of utilizing these techniques does not rely on a final output, i.e., correctly identifying speech or verifying a speaker, but instead uses an interim calculation. For example, speaker verification similarity is used alone, regardless of the result of the comparison with a threshold. In some embodiments, a similarity index is tracked during treatment adjustments, as well as its spontaneous changes and changes and trends associated with treatment adjustments. In some embodiments, when the magnitude of change in the similarity index at a certain treatment level is significantly different from the established trend, it indicates dysarthria. The same is true for the similarity calculated during the speech recognition process, which is used to determine which letter or word (or character or word) is most likely to be spoken. Even if the final classification result does not change, a reduced probability of the correct number exceeding the variability due to spontaneous differences between repetitions of the same pronunciation may also indicate dysarthria.

[0462] According to some exemplary embodiments, the raw features that are input to the classifier are fed into a new classifier, specifically trained to detect dysarthria. In some embodiments, both the raw features and more downstream results of the processing, such as similarity indices or uttered letter probabilities, are fed into a new classifier, which is trained to detect dysarthria.

[0463] According to some exemplary embodiments, in order to train a dysarthria classifier, a database of utterances and their associated labels is first constructed, including speaker identification (speaker #001, #002, etc.) and speaker status – (normal phonation, phonation degraded due to disease symptoms, or dysarthria induced by treatment). In some embodiments, the classifier is trained on this data by one of many machine learning supervised classification methods, such as SVM, decision tree, random forest, naive Bayes, HMM, artificial neural network, etc., to minimize its prediction error. In some embodiments, two settings are possible - the first is that the classifier is only used to detect or reject dysarthria cases, and the second is that the setting needs to distinguish between normal speech, disease-related abnormal speech, or therapeutic dysarthria. In some embodiments, although the second setting is more informative and used in more applications, such as patient diagnosis or patient assessment before taking advanced treatments, it is more difficult to train, requires a larger database, and may result in a larger error rate.

[0464] An example of using basic features to distinguish between recordings of patients with dysarthria and patients without dysarthria is described below. The steps described allow, for example, separation between stimulus groups with dysarthria and stimulus groups without dysarthria. Thus, in some embodiments, given a new recording, it can be classified into one of these groups, and if dysarthria is present, dysarthria can be detected.

[0465] According to some exemplary embodiments, for example, there is a capability to prepare for detecting dysarthria in human speech by recording pronunciation data from a plurality of subjects, wherein each recording is labeled as "dysarthria" or "non-dysarthria."

[0466] According to some exemplary embodiments, an algorithm for detecting dysarthria in a recording is constructed by computing features of the recorded signal.

[0467] According to some exemplary embodiments, a model is defined in which various features are combined into a single number through mathematical relationships, such as a linear combination model or a nonlinear model, such as a generalized linear model (GLM). In some embodiments, the model has several coefficients that are unknown at the beginning.

[0468] According to some exemplary embodiments, the model coefficients, or the exact combination of computed features for each recording, may yield the best separation between the groups of recordings labeled "dysarthria" and "non-dysarthria" (or inferred as "machine learning").

[0469] According to some exemplary embodiments, the generated algorithm is used to detect dysarthria side effects in the patient being evaluated. In some embodiments, pronunciation data is recorded from the patient. In some embodiments, the algorithm is applied to the recorded data to check whether dysarthria exists in the recorded data.

[0470] According to some exemplary embodiments, after the binary decision (e.g., true / false, dysarthria / non-dysarthria) described above, the severity of the side effect of the examined patient is quantified. In some embodiments, this is performed, for example, by measuring the distance between a point in the recorded feature space representing the examined patient and a straight line (or curve, plane, or other geometric entity) representing a threshold between the two groups. In some embodiments, the greater the distance in the feature space, the higher the severity score of the dysarthria assigned to the patient.

[0471] In some embodiments, the voice of a patient who is not undergoing advanced therapy is repeatedly recorded and analyzed over a longer time frame than the DBS procedure itself or the DBS programming period, and changes in the identified results indicate a significant deterioration in vocal activity due to disease progression. Such events may result in the presentation of an indication to a system user, i.e., a caregiver, movement disorder specialist, or a layperson, such as a family member or the patient themselves, suggesting further consultation and possible treatment adjustments.

[0472] Exemplary Gaze Disturbance Assessment

[0473] According to some exemplary embodiments, the assessment system is used to detect gaze abnormalities, such as gaze abnormalities induced by treatment. In some embodiments, at least one sensor of the system is configured to track the range of motion of the patient's eyeballs and detect gaze abnormalities induced by treatment. In DBS settings, a common gaze abnormality is a restriction of eye movement, which should be tested during an active task that moves the eyes as far to the sides as possible. In some embodiments, to achieve this goal, the sensor used may include an eye tracking device, such as described in www.c.cmu.edu / ~ltrutoiu / pdfs / ISWC\u2016\utrutoiu.pdf. In some embodiments, the eye tracking device includes a video eye tracker, such as a video eye tracker based on infrared (IR) light directed at the eyeball, locating the identified corneal reflection (CR, first Perkins image) and using the vector between the center of the pupil or the center of the iris and the CR to infer the gaze direction. In some embodiments, the video eye tracker does not use infrared light, but locates the eyes and pupils / irises in the image based on image processing, and then calculates the gaze direction based on the pupil position and / or visible shape. In some embodiments, a calibration step is performed for any of these methods where the patient performs a predefined set of eye movements at a baseline treatment level.

[0474] According to some exemplary embodiments, a technique based on recording an electrooculogram (EOG) is used, i.e., recording the voltage between two or more surface electrodes on the skin around the eye, which is affected by a dipole between the negatively charged retina and cornea. When the eye moves, the dipole rotates and the voltage between a pair of surface electrodes changes accordingly, for example becoming negative or positive depending on the direction of the eye movement. In some embodiments, this method allows measuring the maximum voltage deviation obtained when each eye moves to each extreme position (left, right, up, down) at baseline, and then comparing the EOG voltage deviation during treatment. In some embodiments, when the voltage deviation of one eye is similar to the baseline deviation, while the voltage deviation of the other eye fails to reach the baseline deviation, this indicates a gaze abnormality induced by the treatment.

[0475] According to some exemplary embodiments, the position of the eyeball can be tracked by installing embedded mirrors or magnetic field sensors on soft contact lenses and tracking their positions using cameras or electromagnetic coils.

[0476] Exemplary gaze analysis

[0477] According to some exemplary embodiments, in order to measure a signal for assessing gaze, e.g. Fig.14A As shown, at least two electrodes, such as EMG electrodes, are placed on one side of the face near the eyes. In some embodiments, electrodes are positioned near the eyes, such as position 1502 and position 1504, to measure the activity of muscles associated with eye movements. In some embodiments, at least one reference is positioned at other locations on the face or body. In some embodiments, at least one reference electrode is positioned on the frontal bone, just above the nasion point 1506.

[0478] According to some exemplary embodiments, in order to detect the movement of the eyeball, the potential difference between the steady-state voltage and the voltage at the signal peak point pf is measured. In some embodiments, in order to measure the values ​​of these points, for example Fig. 14B As shown, an algorithm for detecting the starting eye movement point (switching point) value and its associated peak value is used. In some embodiments, for the detection of gaze palsy, the step length value measured at the baseline (no stimulation) is compared with the step length value measured during stimulation. In some embodiments, an indication of gaze palsy is received when the difference is above a predetermined tolerance.

[0479] Another approach is to establish a baseline step size for each side (eye) and calculate the current step size for each eye based on the stimulus level and compare it to the baseline. When only one eye's value deviates significantly from the baseline, while the second eye's value is consistent with the baseline, it indicates fixation paresis.

[0480] FIG. 14C to FIG. 14IResults of an exemplary gaze analysis using a signal processing method for gaze detection are described.

[0481] In the gaze analysis and in some embodiments, the received data is passed through a bandpass filter (e.g., Butterworth order = 2, cutoff frequency = 0.1 to 20 Hz). Then, in some embodiments, for example Fig. 14C As shown, the signal is smoothed by using a Gaussian kernel function.

[0482] In the gaze analysis and in some embodiments, after smoothing, the signal is divided into segments of equal size, for example, segments of 50 milliseconds duration. Then, in some embodiments, multiple fitting functions are applied to each segment, for example, receiving the nearest trend line, which means fitting a linear approximation for each segment.

[0483] In the gaze analysis and in some embodiments, the switching points are then identified, for example, by checking that the line slope of the linear approximation of each segment is greater than a predetermined value. After detecting the switching points, the peak values ​​are then measured.

[0484] Fig.14D The result of signal smoothing is depicted. Fig.14E The results of applying multiple fit functions to the selected segments are shown.

[0485] In the graph showing the results, the algorithm was set to ignore selected durations between the switching point and the peak point greater than 1 second, step values ​​less than 35uV, and half maximum peak widths less than 0.4 seconds, optionally indicating flicker. Fig.14F The results of the signal processing method showing one side movement are described.

[0486] In the analysis and in some embodiments, to detect the movement of the pupil of the eye to the side in two or more steps, we add the values ​​of these steps. This allows to obtain the full step value. That is, when the patient moves the pupil of the eye in one step. In some embodiments, the algorithm adds the values ​​of adjacent steps with the same slope direction.

[0487] Figure 14G The results of a signal processing method for displaying eye movements in 2 steps are described.

[0488] Fig.14H Results of a signal processing approach using data from actual surgery are described. Fig.14I The full range of results using signal processing methods is described.

[0489] Example of internal capsule recruitment assessment

[0490] According to some exemplary embodiments, an algorithm is used to detect movement caused by recruitment of the internal capsule, such as movement caused by electrical stimulation. In some embodiments, the algorithm is used to detect action movement of facial muscles (artificial activation due to leakage current), which is a side effect often encountered during DBS surgery or IPG programming. Alternatively or additionally, the algorithm can also be used to detect contraction of upper or lower extremity muscles, which is also a side effect of DBS.

[0491] According to some exemplary embodiments, the algorithm is based on the electromyographic signals recorded from the zygomatic muscles on the side of the mouth (left or right) and a reference electrode in the middle of the forehead, such as Fig.16A shown. Fig.15A The EMG electrodes are shown to be located outside the corners of the subject's mouth, such as on the left and right zygomatic muscles. In addition, a reference electrode is located on the body or face, such as on the frontal bone just above the nasion point 1606.

[0492] According to some exemplary embodiments, the evaluation and analysis method using the algorithm comprises the following steps.

[0493] According to some exemplary embodiments, data is recorded before stimulation, for example, within a time period of up to 10 minutes before stimulation or any shorter or longer time period and during stimulation. In some embodiments, a low-pass filter, such as a Butter-worth three-pole filter, is applied to the signal, for example to remove stimulation artifacts between 2 and 100 Hz. In some embodiments, the mean and standard deviation (STD) of the differences in the data recorded before stimulation are calculated. Additionally or alternatively, the median and median absolute deviation (MAD) of the data or similar centrality and variability indices are calculated. In some embodiments, a point is identified when the difference value (of the data during stimulation) reaches a value above the mean + 3 times the standard deviation (or another threshold value that defines a substantial deviation from the "center" of the data, such as the median and MAD), for example to detect the beginning of movement and when it decreases.

[0494] Fig. 15B Results of the analysis of the left oral passage are described. Fig. 15C Results for the right oral passage are depicted, where action motion is detected, with two arrows indicating two points detected by the algorithm.

[0495] Exemplary Signal Preprocessing

[0496] According to some exemplary embodiments, preprocessing of the signal acquired by at least one sensor includes one or more of mean subtraction, normalization or standardization, analysis of components by principal component analysis (PCA) or independent component analysis (ICA), or filtering according to frequency domain characteristics using fixed or adaptive filters. In some embodiments, one purpose of the preprocessing is to detect the moment when DBS is given, or the moment when the configuration is changed. In some embodiments, this is done by identifying stimulation artifacts in the signal, which are optionally caused by electromagnetic interference between the magnetic field associated with the stimulation current and the recorded signal, characterized by spikes in the frequency domain with many higher harmonics. In some embodiments, another purpose is to minimize the impact of the stimulation artifact so that the clean signal can be further processed. In some embodiments, this can be done by filtering, fixed or adaptive or by a pattern recognition process. In the latter, repetitions of the appearance of the artifact are identified, a prototype artifact is constructed from this signal set, and then the prototype artifact is subtracted from the signal at each time point where the artifact is identified. In some embodiments, other objectives are to emphasize or attenuate specific features in the signal, such as rhythmic low-frequency oscillations associated with tremor (e.g., in the range of 4 to 6 Hz, or any smaller or larger value), and / or to normalize the amplitude so that features extracted from different subjects are comparable.

[0497] Exemplary construction features

[0498] According to some exemplary embodiments, one way to construct a signal signature is to use knowledge and intuition about the recorded signal and the properties that one wishes to quantify. In some embodiments, the signal signature is composed of one or more of the following groups: fundamental tremor frequency ft, defined as the frequency with the highest power density in a frequency band [fa, fb], where fa≤ft≤fb, during rest; movement frequency fm, which is the frequency with the largest increase in power density when switching from rest to active movement, and can be optionally calculated from the original signal, the filtered signal, or a low-frequency modulation envelope calculated from the signal; power density at the fundamental tremor frequency ft, normalized to the power in the frequency band [f0, f1], where f0≤ft≤f1; total harmonic distortion (THD) relative to the fundamental tremor frequency; and total harmonic distortion (THD) above the fundamental tremor frequency. the total power in frequencies above fhi (fhi = 15, 20, or 25 Hz); the highest frequency at which the power density is greater than 5% of the maximum power density; the temporal correlation between the power in frequencies above fhi and below fhi; the temporal correlation between pairs of signals or signal envelopes recorded from various muscles; the maximum cross-correlation value between pairs of signals or signal envelopes recorded from various muscles; the time delays for the maximum cross-correlation values ​​calculated for pairs of signals or signal envelopes recorded from various muscles; the time difference between the application of the stimulus and the onset of each of the other features; and the degree of nonstationarity of the features—how much the mean and variance of each feature vary over time

[0499] For example, in some embodiments, Parkinson's tremor is expected to be associated with a fundamental frequency of 4 to 5 Hz, high power density at ft, high THD relative to ft, high correlation between high frequency (greater than 20 Hz) and low frequency band power, and high cross-correlation between limbs. In some embodiments, expected movement disorders are associated with low power density in ft, high power in frequencies above fhi, large non-stationarity, and low correlation and cross-correlation between limbs. In some embodiments, the degree of rigidity is associated with high power in frequencies above fhi, low correlation between high frequency band and low frequency band, low non-stationarity, and high cross-correlation between limbs. In some embodiments, tremor, movement disorders, and rigidity symptoms are most obvious when the patient is at rest because they occur spontaneously and are not related to intentional movement.

[0500] Conversely, in some embodiments, bradykinesia is evident when the patient performs a motor task. Features that quantify bradykinesia include envelope frequency, which in some embodiments increases the most power density when comparing signals recorded during repetitive motor tasks to signals recorded during rest. In some embodiments, the more severe the bradykinesia, the lower the frequency of movement.

[0501] According to some exemplary embodiments, the motor recruitment side effect caused by DBS is the result of the current reaching and activating the corticospinal tract of the internal capsule or the corticobulbar tract, resulting in activation of lower limb motor neurons and contraction of limb or facial muscles. In some embodiments, these contractions are recorded in the electromyogram of the activated muscles, and the features in the electromyogram have a high temporal correlation with the onset and offset of the stimulation, and the amplitude and energy of the electromyogram increase with increasing stimulation levels. According to some exemplary embodiments, the relationship between the increase in electromyogram and the greater DBS level is opposite to the relationship between the DBS level and the electromyogram associated with tremor or rigidity, which generally decreases with increasing DBS levels. In addition, when the stimulating electrode is placed in the target nucleus, the symptoms are generally alleviated before the occurrence of side effects, including motor recruitment. In some embodiments, this sequential structure is used to distinguish EMG signals related to symptoms, such as rigidity, from EMG signals caused by side effects.

[0502] According to some exemplary embodiments, the above expectations are used to define and construct signal features, and the calculation of an index for each attribute based on the features is statistically inferred from a database comprising 1, 2, ..., M features calculated for each of N subjects, and optionally individual assessments by experts for each attribute.

[0503] According to some exemplary embodiments, another way to construct features is to generate a large library of features without considering intuition or prior knowledge. In some embodiments, a database with expert evaluations of features and attributes is used to statistically highlight features with high predictive value for various attributes. Examples of functions of the library include signal projections on PCA principal components, power in frequency bands, such as in the frequency bands [1 to 5 Hz], [5 to 10 Hz], [15 to 20 Hz], etc., and the distance between the first quartile and the third quartile of the signal amplitude.

[0504] Exemplary Fixed and Adaptive Filtering

[0505] According to some exemplary embodiments, fixed filtering is the application of a predefined filter to the data, while adaptive filtering means that the filter depends on the signal characteristics. For example, in order to obtain a signal with relatively less jarring low-frequency components, a fixed 4- or 8-pole Butterworth IIR high-pass filter with a cutoff frequency fc=20 Hz is used, or a fixed high-order (e.g. N=2345) FIR high-pass filter with a cutoff frequency fc=20 Hz is used. In some embodiments, adaptive filtering for the same goal begins with a spectral density analysis of the input signal and locating the fundamental frequency ft based on the highest power density between 2 and 6 Hz. After this stage, the IIR or FIR filter is designed to have a cutoff frequency of 5ft, thereby filtering out the first 5 harmonics of ft.

[0506] Example display

[0507] According to some exemplary embodiments, the evaluation system includes a display, such as a treatment space assessment (TSA) display. In some embodiments, the display software (SW) is a user interface that is optionally connected to the DBS via wireless transmission. In some embodiments, the display software collects data from the system online and runs different analysis functions to evaluate the patient's condition, such as evaluating rigidity, tremor, bradykinesia, motor recruitment, gaze and speech. In some embodiments, the display software displays and saves the results in digital and / or graphical form. In some embodiments, the display software has a setting window for users to edit software settings. In some embodiments, the display software enables users to record their clinical feedback, or insert any other data into the software, including an assessment of symptoms and side effects that are not quantified by the system. In some embodiments, the display software presents a comparison table that compares the treatment space assessment (TSA) results with clinical feedback and / or rankings or scores of different measurements.

[0508] According to some exemplary embodiments, the display software is divided into four main windows:

[0509] 1. For example Fig.16AShown is a startup window that allows, for example, connecting to a DBS system and / or defining EMG and sensor signal mappings.

[0510] 2. For example Fig. 16B A TSA display shown includes at least one display adapter, a setting window and a save button. In addition, the TSA display includes a user interface for allowing access to a clinical feedback input window.

[0511] 3. A clinical feedback input user interface, e.g. Fig. 16C Shown

[0512] 4. A summary table, e.g. allowing comparison of TSA results and clinical feedback, e.g. Fig.16D shown.

[0513] It is expected that many related DBS systems will be developed during the life of the patent expiring in this application, and the scope of the term DBS system as used herein is intended to preferentially include all such new technologies. The term "about" as used herein with respect to a quantity or value means "within the range of ±10%".

[0514] As used herein, the terms "comprises," "comprising," "includes," "including," "having" and conjugations thereof mean "including but not limited to."

[0515] As used herein, the term "consisting of" means including and limited to.

[0516] As used herein, the singular form "a," "an," and "at least one" include plural references unless the context clearly dictates otherwise.

[0517] As used herein, the term "consisting essentially of" means that a composition, method, or process may include additional ingredients and / or steps, but only if the additional ingredients and / or steps do not materially alter the basic and novel characteristics of the claimed composition or method.

[0518] As used herein, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a compound" or "at least one compound" as used herein may include a plurality of compounds, including mixtures thereof.

[0519] Throughout this application, various embodiments of the present invention may be presented in the form of a range. It should be understood that the description in the form of a range is only for convenience and brevity and should not be construed as a hard limitation on the scope of the present invention. Therefore, it should be considered that the range description has specifically disclosed all possible sub-ranges and single values ​​within the range. For example, it should be considered that the range description from 1 to 6 has specifically disclosed sub-ranges, such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as single numbers within the numbered range, such as 1, 2, 3, 4, 5 and 6, which applies regardless of the range.

[0520] Whenever a numerical range is indicated herein (e.g., "10 to 15," "10 to 15," or any pair of numbers connected by these other such range indications), unless the context clearly indicates otherwise, it is meant to include the indicated range limits, including any numbers (fractional or integral) within the range limits.

[0521] As used herein, the terms "range / range of / range between" meaning between a first indicative number and a second indicative number, and "range / range of / range starting from..." meaning from the first indicative number "to", "until", "up to" or "covering" (or another such range indicating term) the second indicative number, are used interchangeably herein and are meant to include the first and second indicative numbers and all decimals and integers therebetween.

[0522] Unless otherwise indicated, the numbers used herein and any numerical ranges based thereon are approximations within the precision of reasonable measurements and rounding errors, as will be understood by those skilled in the art.

[0523] As used herein, the term "method" refers to manners, means, techniques and procedures for accomplishing a particular task, including but not limited to those manners, means, techniques and procedures that are known or readily developed by practitioners in the chemical, pharmacological, biological, biochemical and medical fields from known manners, means, techniques or procedures.

[0524] As used herein, the term "treating" includes abrogating, substantially inhibiting, slowing or reversing the progression of a condition, substantially ameliorating clinical or aesthetic symptoms of a condition, or substantially preventing the appearance of clinical or aesthetic symptoms of a condition.

[0525] It should be understood that certain features of the present invention, which for clarity are described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present invention, which for brevity are described in the context of a single embodiment, may also be provided separately, or in any suitable subcombination, or in any other described embodiment applicable to the present invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment does not function without those elements.

[0526] Although the present invention is described in conjunction with its specific embodiments, it is obvious that many substitutions, modifications and variations will be apparent to those skilled in the art. Therefore, it is intended to embrace all substitutions, modifications and variations that fall within the spirit and broad scope of the appended claims.

[0527] All publications, patents and patent applications mentioned in this specification are incorporated herein by reference in their entirety. The extent to which each individual publication, patent or patent application is specifically and individually indicated is incorporated herein by reference. In addition, any reference cited or indicated should not be construed as admitting that these references can be used as prior art of the present invention. The title part in this application is used in this article to make this specification easy to understand and should not be construed as a necessary limitation.

[0528] In addition, any priority documents of the present application are hereby incorporated by reference.

Claims

1. A treatment space mapping method, characterized in that: The method comprises: a. delivering stimulation at at least one location within the brain using at least one combination of treatment parameter values, receiving a plurality of signals associated with a patient condition from at least one sensor during and / or after at least one brain stimulation; b. analyzing the received plurality of signals to provide a quantitative assessment of at least one therapeutic side effect and at least one symptomatic effect; and c. Mapping the treatment space based on the quantitative assessment.

2. The method according to claim 1, characterized in that: The mapping includes mapping the treatment space based on a desired future flexibility.

3. The method according to claim 1, characterized in that: The method further includes recording the plurality of signals while the patient is at rest.

4. The method according to claim 1, characterized in that: The method further includes recording the plurality of signals while the patient performs a task.

5. The method according to claim 1, characterized in that: The method further includes determining that a stimulation electrode or an electrode lead is located at a correct position within the brain.

6. The method according to claim 1, characterized in that: At least one combination of treatment parameter values ​​is selected based on the mapping of the treatment space.

7. The method according to claim 1, characterized in that: The method further includes transmitting an indication regarding the treatment space.

8. The method according to claim 1, characterized in that: The received plurality of signals are measured during a deep brain stimulation (DBS) lead implantation procedure in an operating room and / or after at least one brain stimulation in the operating room.

9. The method according to claim 1, characterized in that: The analysis is based on stored continuously measured signals.

10. The method according to claim 1, characterized in that: The method further includes selecting at least one combination of treatment parameter values ​​based on the quantitative assessment of the treatment side effects and the at least one symptomatic effect measured during the electrode lead implantation procedure.

11. The method according to claim 1, characterized in that: The method further includes communicating, via a user interface, an indication of the at least one therapeutic side effect and the at least one symptomatic effect.

12. The method according to claim 2, characterized in that: The method further comprises calculating at least one value of the future flexibility based on the quantitative assessment of a combination of the at least one treatment side effect and the at least one symptomatic effect and / or the at least one treatment parameter value.

13. The method according to claim 12, characterized in that: The method further includes generating a treatment space based on the at least one future flexibility value, the quantitative assessment of at least one side effect and the at least one treatment side effect, and the at least one treatment parameter value combination for the at least one stimulation.

14. The method of claim 1, wherein: The method further includes transmitting a signal to a user interface to convey an indication of the mapped treatment space, wherein the treatment space is a multi-dimensional space defined by two or more treatment parameter values ​​that promote a desired treatment effect and a desired level of side effects.

15. The method according to claim 14, characterized in that: The method further includes calculating at least one selectable combination of treatment parameter values ​​based on the mapped treatment space.

16. The method of claim 15, wherein: The method further includes sending a signal to the user interface to convey an indication associated with the at least one selectable combination of treatment parameter values.

17. The method of claim 14, wherein: The method further includes calculating a relationship between at least one combination of treatment parameter values ​​and the mapped treatment space, and signaling the user interface to convey an indication of the relationship.

18. The method of claim 1, wherein: The mapping is based on at least one desired future flexibility range or score.

19. The method of claim 18, wherein: The method further includes calculating the at least one value of the future flexibility based on a future effect of at least one treatment effect modifier, wherein the at least one treatment effect modifier includes disease progression, future variables of treatment side effects, future variables of disease symptoms, future variables of stimulation locations, future variables of the number and / or combination of stimulation electrodes, and drug usage patterns.

20. The method of claim 1, wherein: The method further includes generating a graphical representation of a level of future effect of the at least one therapeutic effect modifier.

21. The method of claim 20, wherein: The method further includes receiving at least one value related to the future flexibility from a remote database.

22. The method of claim 20, wherein: At least one value related to the future flexibility is calculated based on a large data set collected from a plurality of patients.

23. The method of claim 1, wherein: The method further includes displaying a list of a plurality of treatment parameter value combinations suitable for delivering brain stimulation based on the at least one treatment side effect and the at least one symptomatic effect.

24. The method of claim 1, wherein: The method further includes displaying a graphical representation of the generated treatment space around the at least one combination of treatment parameter values ​​for the at least one stimulation.

25. The method of claim 1, wherein: The method further includes generating a score for each of a plurality of treatment effect modifiers, and further includes displaying a graphical representation of the generated score associated with the treatment effect modifier.

26. The method of claim 1, wherein: The at least one treatment side effect includes gaze deviation, double vision, persistent activation of leg, arm or facial muscles, and movement disorders.

27. The method of claim 1, wherein: The at least one symptomatic effect includes one or more of muscle rigidity, tremor, and bradykinesia.

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