Automatic calibration method and system for local field potential sensing tool for deep brain stimulation

Abstract

A system for automatic calibration of local field potential (LFP) sensing for deep brain stimulation may include one or more electrodes, and a control circuit. The control circuit is configured to sense an LFP of the brain using the one or more electrodes prior to delivery of electrical stimulation via the one or more electrodes, record a snapshot of LFP activity within a broad band range, determine a peak frequency of LFP activity from the snapshot, and update a frequency of interest (FOI) based on the peak frequency.

Description

[0001] This application claims the priority of U.S. Provisional Patent Application 63 / 433,331, filed on December 16, 2022, the entire content of which is incorporated herein by reference. Technical Field

[0002] The present invention generally relates to the automatic calibration of local field potential (LFP) sensing tools to assist in the configuration and application of LFP sensing in deep brain stimulation (DBS) practices. Background Art

[0003] Implantable medical devices, such as electrical stimulators or therapeutic agent delivery devices, have been proposed for different therapeutic applications, such as deep brain stimulation. In some therapeutic systems, an implantable electrical stimulator delivers electrical therapy to a target tissue site in a patient's body via one or more electrodes, which can be deployed through a medical lead and / or on the housing of the electrical stimulator, or both. In some therapeutic systems, therapy can be delivered via a specific combination of electrodes carried by the lead and / or the housing of the electrical stimulator.

[0004] During a programming session, which can occur during the implantation of the medical device, during a trial session, or during an in-clinic or remote follow-up session after the medical device has been implanted in the patient, a clinician can generate one or more treatment programs (also referred to as treatment parameter sets) that are found to provide effective treatment to the patient, where each treatment program can define a set of values for treatment parameters.

[0005] In the case of DBS, although DBS systems have the ability to record local field potentials (LFP) in the brain, they still rely on users for manual calibration. Specifically, to ensure the proper operation of various sensing tools, the frequency of interest (FOI) or set of FOIs (each lead corresponds to a unique FOI) must be correctly selected. This poses a persistent technical challenge for users because, during the configuration phase, the signal visibility of the FOI may not always be available for selection.

[0006] In the conventional mode, a clinician needs to manually calibrate the LFP in two ways during a follow-up session: one is to analyze LFP snapshots manually triggered by the patient previously, and the other is to observe the correlation between the tracked FOI power and the severity of the patient's clinical symptoms. This poses a persistent challenge for users because determining the appropriate LFP calibration typically requires a clinician to conduct several clinical follow-ups to determine the effectiveness of the previously set FOI. Therefore, for DBS, one of the core bottlenecks in recording valuable LFP data lies in the operational burden brought about by the configuration process.

[0007] Therefore, it is necessary to improve the calibration efficiency of LFP sensing tools while reducing the user burden. Summary of the Invention

[0008] The disclosed technology generally relates to a method for automatically calibrating an LFP sensing tool prior to delivering DBS treatment. This calibration is performed during the period between implanting the neurostimulator and initial programming, which can detect the patient's FOI and automatically adjust the system's tracking of that frequency, thereby improving the efficiency of the initial programming visit and reducing the patient's burden.

[0009] In one aspect, the present disclosure provides a system for automatically calibrating local field potential (LFP) sensing for deep brain stimulation. The system may include one or more electrodes and control circuitry. The control circuitry is configured to sense the LFP of the brain at the frequency of interest (FOI) using the one or more electrodes before delivering electrical stimulation via the one or more electrodes, capture a snapshot of the LFP activity over a wide frequency band, determine the frequency of the LFP peak activity from the snapshot, and update the FOI based on the peak activity frequency.

[0010] In another aspect, the present disclosure provides a method for automatically calibrating LFP sensing for deep brain stimulation. The method includes, before delivering electrical stimulation, sensing the LFP of the brain at the FOI using one or more electrodes, capturing a snapshot of the LFP activity at a wide frequency band, determining the frequency of the LFP peak activity from the snapshot, and updating the FOI based on the peak activity frequency.

[0011] In yet another aspect, the present disclosure provides a method for calibrating a series of electrodes. Different electrode combinations can be selected from the series of electrodes as sensing electrodes. Then, the sensing electrodes can be automatically updated based on the electrodes having the maximum LFP activity at the FOI. Thus, it should be understood that the described operations can be repeated on several electrodes, enabling sequential or synchronous evaluation of various electrode combinations.

[0012] Details of one or more aspects of the present disclosure are set forth in the following drawings and description. Other features, objects, and advantages of the technology described in the present disclosure will be apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a conceptual diagram showing an example system for providing deep brain stimulation according to one embodiment.

[0014] Figure 2 is a conceptual diagram showing an example system for providing robust adaptive brain stimulation according to one embodiment.

[0015] Figure 3A is a schematic diagram showing an example deep brain stimulation (DBS) system configured to deliver an electrical stimulation treatment to a tissue site in a patient's brain.

[0016] Figure 3B is a block diagram showing the components of a system according to one embodiment Figure 3A of the system.

[0017] Figure 4 is an LFP snapshot of a DBS system according to one embodiment

[0018] Figure 5 is a flowchart of a method for calibrating an LFP sensing tool according to one embodiment

[0019] While various embodiments may adopt various modifications and alternative forms, details thereof have been shown by way of example in the drawings and will be described in detail. However, it should be understood that the intention is not to limit the claimed invention to the particular embodiments described. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the subject matter as defined by the claims. DETAILED DESCRIPTION

[0020] Embodiments of the present disclosure can enable automatic calibration of local field potential (LFP) sensing tools in deep brain stimulation (DBS) practice by utilizing full-spectrum LFP snapshot data collected during the period between neurostimulator implantation and initial patient programming. During this exploration phase before initial programming, the neurostimulator or the patient can trigger full-spectrum LFP snapshots, thereby revealing the correct frequencies of interest (FOI) data that should be tracked. Subsequently, the tracked FOI data plus additional full-spectrum LFP snapshot data can be used to automatically update the FOI, thus completing the configuration process. The increased detail in the data obtained during this exploration phase can also provide a basis for further outputs during the initial programming visit, including circadian rhythm detection, threshold recommendations, and artifact detection or reduction.

[0021] LFP snapshots reveal neural activities within a wide frequency spectrum, and this information can be used to reveal the frequencies (FOI) that longitudinal tools (e.g., timeline tools, streaming tools) should track. Longitudinal tracking tools based on FOI can plot the curve of FOI power over time. Therefore, as a more precise (30s) assessment means, LFP snapshots can capture multiple frequencies that may exist during this period, and this technology can be used in embodiments of the present disclosure to automatically adjust the FOI without clinician visits, thus optimizing the calibration process for clinicians and patients - both accelerating the speed and reducing the complexity.

[0022] As used herein, an LFP snapshot refers to a broadband LFP power spectral density recording, and an LFP timeline is the integrated power in a defined frequency band around the peak FOI. One advantage of broadband LFP snapshot data is that it is independent of the FOI and can capture all potential FOIs at that moment. As envisioned herein, LFP snapshots can be recorded via sensing electrodes with an implantable medical device.

[0023] See also Figure 1 , which depicts a conceptual diagram of an example system 100 that provides deep brain stimulation and can be configured to automatically calibrate a sensing engine for LFP in accordance with one embodiment. In one embodiment, system 100 includes an implantable medical device (IMD) 102 for a patient 104.

[0024] The IMD 102 delivers a nerve stimulation therapy to the patient 104 at a target tissue site. For example, one or more leads can extend from the IMD 102 to the patient 104's brain, and the IMD 102 can deliver a deep brain stimulation (DBS) therapy to the patient 104 to treat, for example, a neurodegenerative disease or trauma.

[0025] The IMD 102 is configured to deliver therapy according to one or more programs or control strategies. A control strategy includes one or more parameters that define an aspect of the therapy delivered by the medical device according to the control strategy. For example, a control strategy that controls the IMD 102 to deliver a stimulation therapy in pulses can define the voltage or current pulse amplitude, pulse width, pulse frequency, electrode configuration, or electric field distribution pattern delivered by the IMD 102 according to the control strategy, or the timing scheme or combination pattern of the stimulation pulses. Additionally, each lead includes a number of electrodes, and the control strategy parameters for controlling the IMD 102 to deliver a stimulation therapy can include information identifying which electrodes have been selected for delivering pulses according to a program, and the polarities of the selected electrodes, i.e., the electrode configuration for the control strategy. Control strategies for controlling the IMD 102 to deliver other therapies can incorporate other parameters according to the specific strategy requirements. Based on LFP sensing, and specifically based on the FOI, the selected control strategy of the IMD 102 is implemented. Thus, achieving optimal DBS efficacy depends on calibrating the LFP sensing engine of the IMD 102.

[0026] In an embodiment (such as the one depicted), system 100 also includes an external device 106. The external device 106 is communicatively coupled to the IMD 102 via wireless communication. The external device 106 can be a device for inputting information related to the patient 104, programming the IMD 102, receiving information from the IMD 102, and updating the IMD 102 (such as updating the FOI).

[0027] See alsoFigure 2 , depicts a conceptual diagram showing an example system for providing robust adaptive brain stimulation according to one embodiment. As depicted, external devices 106a and 106b are further shown as a handheld computing device and a laptop computing device, but may further be a key card or a watch, a smart phone, a computer workstation, or a networked computing device, etc. Embodiments of the system may also include a server 108 and / or a database 110, as shown in the figure.

[0028] The server 108 may include one or more servers in a cloud computing environment. The server 108 may be configured to communicate wirelessly with the external devices 106a and / or 106b, and / or the IMD 102. In an embodiment, the server 108 may be co-located with the external devices 106a and / or 106b, or located elsewhere, such as in a cloud computing data center or a medical clinic.

[0029] The database 110 is configured to store data related to the system 100, including IMD 102 and / or external device 106 data. In an embodiment, the database 110 may be integrated as part of the server 108, or may be stand-alone, such that the server 110 and / or the external devices 106a and 106b may be communicatively coupled to the database 110. The database 110 may be a general-purpose database management storage system (DBMS) or a relational DBMS, such as a system implemented by Oracle, IBM DB2, Microsoft SQL Server, PostgreSQL, MySQL, SQLite, Linux, or Unix solutions.

[0030] One purpose of the database 110 is to store LFP snapshot data and programmer event traces, which may be used as needed to update the FOI or verify the FOI threshold. The LFP activity transmitted to the server 108 can be an effective way to evaluate the effectiveness of the settings and train a machine learning algorithm (MLA) to automatically extract noise from the LFP timeline.

[0031] In an embodiment, the database 110 stores LFP snapshots, LFP timeline data, and stimulation timeline data. These data can be used to train and automatically set LFP thresholds and stimulation limits. For example, the upper and lower limits of stimulation for adaptive DBS can be automatically set based on the patient's use and adaptation to the stimulation.

[0032] See Figure 3A , depicts an alternative illustration of the system 100. In Figure 3AIn the illustrated example, the treatment system 100 includes a medical device programmer as an external device 106, a nerve stimulator as an implantable medical device (IMD) 102, a lead extension 112, and one or more leads 114 having respective electrode arrays 116. The IMD 102 includes a stimulation generator configured to generate an electrical stimulation therapy and deliver the electrical stimulation therapy to one or more regions of a patient's 104 brain via one or more electrodes 116 of the one or more leads 114, respectively.

[0033] DBS can be used to treat or manage a variety of patient conditions, such as but not limited to seizures (e.g., epilepsy), pain, migraines, mental disorders (e.g., major depressive disorder (MDD), bipolar disorder, anxiety disorder, post-traumatic stress disorder, dysthymia, and obsessive-compulsive disorder (OCD)), behavioral disorders, mood disorders, memory disorders, psychomotor disorders, movement disorders (e.g., essential tremor or Parkinson's disease), Huntington's disease, Alzheimer's disease, or other neurological or psychiatric disorders and impairments of patient 104.

[0034] The lead 114 can be positioned to deliver an electrical stimulation therapy to one or more target tissue sites within the brain to manage patient symptoms associated with the patient's 104 disorder. The lead 114 can be implanted via any suitable technique (such as through a respective borehole in the patient's 104 skull or through a common borehole in the calvarium) to position the electrode 116 at a desired location within the brain. The lead 114 can be placed at any location within the brain such that the electrode 116 can provide electrical stimulation to a target therapy delivery site within the brain during treatment. Different neurological, motor, or psychiatric disorders can be associated with activity in one or more of the brain regions, which can vary among patients. Thus, the target therapy delivery site for the electrical stimulation therapy delivered by the lead 114 can be selected based on the patient condition. For example, suitable target therapy delivery sites within the brain for controlling a movement disorder of patient 104 can include one or more of the following: the pedunculopontine nucleus (PPN), the thalamus, basal ganglia structures (e.g., the globus pallidus, the substantia nigra, or the subthalamic nucleus (STN)), the zona incerta, the fasciculus, the lenticular fasciculus (and its branches), the lenticular loop, or Forel's field (thalamic fasciculus). The PPN can also be referred to as the tegmental nucleus of the pons.

[0035] As described above, the IMD 102 can deliver an electrical stimulation treatment to the brain of the patient 104 according to one or more control strategies. The control strategy can define one or more electrical stimulation parameter values of the treatment that are generated by the stimulation generator of the IMD 102 and delivered from the IMD 102 to the target treatment delivery site in the body of the patient 104 via one or more electrodes 116. The electrical stimulation parameters can define an aspect of the electrical stimulation treatment and can include, for example, the voltage or current amplitude of the electrical stimulation signal, the charge level of the electrical stimulation, the frequency of the electrical stimulation signal, the waveform shape, the on / off cycle state (e.g., if the cycle is "off", the stimulation is always on, and if the cycle is "on", the stimulation is cyclically turned on and off), and in the case of an electrical stimulation pulse, the pulse frequency, pulse width, and other suitable parameters (such as duration or duty cycle). Additionally, if different electrodes can be used to deliver the stimulation, the treatment parameters of the treatment program can be further characterized by the electrode combination, which can define the selected electrodes 116 and their corresponding polarities. In some examples, the stimulation can be delivered using a continuous waveform, and the stimulation parameters can define the waveform.

[0036] In addition to being configured to deliver a treatment to manage the patient's 104 disorder, the treatment system 100 can also be configured to sense the brain bioelectrical signals or another physiological parameter of the patient 104. For example, the IMD 102 can include a sensing engine configured to sense brain bioelectrical signals within one or more regions of the brain via the electrodes 116. Thus, in some examples, the electrodes 116 can be used to deliver electrical stimulation to a target site within the brain and to sense brain signals. However, the IMD 102 can also use a separate set of sensing electrodes to sense the brain bioelectrical signals. In some examples, the sensing engine of the IMD 102 can sense the brain bioelectrical signals via one or more of the electrodes 116 that are also used to deliver electrical stimulation to the brain. In other examples, one or more of the electrodes 116 can be used to sense the brain bioelectrical signals, while one or more different electrodes 116 can be used to deliver the electrical stimulation. The sensing engine can be used to record LFP snapshots.

[0037] It is noted that during the period between the implantation of the neurostimulator and the initial programming of the patient, the stimulation function has not been turned on for the patient, and the user has greater flexibility in selecting the sensing configuration. The selection of the optimal sensing electrodes can be made without considering the location of the unactivated stimulation electrodes, and any FOI can be selected for tracking. During this exploration phase when the patient is at home, a full-spectrum LFP snapshot can be triggered, thereby revealing the correct FOI to be tracked. At the initial programming, the clinician can confirm the automatically detected FOI and initiate the stimulation treatment.

[0038] The external medical device programmer 106 is configured to wirelessly communicate with the IMD 102 as needed to provide or retrieve therapy information. The programmer 106 is an external computing device that can be used by, for example, a clinician and / or the patient 104 to communicate with the IMD 102. For example, the programmer 106 can be a clinician programmer that a clinician uses to communicate with the IMD 102 and program one or more therapy programs for the IMD 102. Additionally or alternatively, the programmer 106 can be a patient programmer that allows the patient 104 to select programs and / or view and modify therapy parameter values. The clinician programmer can include more programming features than the patient programmer. In other words, for more complex or sensitive tasks, programming of programmer events may only be allowed via the clinician programmer to prevent untrained patients from making unintended modifications to the IMD 102.

[0039] The programmer 106 can be a handheld computing device having a display viewable by a user and an interface for providing input (i.e., a user input mechanism). For example, the programmer 106 can include a small display screen (e.g., a liquid crystal display (LCD) or a light emitting diode (LED) display) that presents information to the user. Additionally, the programmer 106 can include a touch screen display, a keypad, buttons, a peripheral pointing device, voice activation, or another input mechanism that allows the user to navigate and provide input via the user interface of the programmer 106. If the programmer 106 includes buttons and a keypad, the buttons can be dedicated to performing specific functions (e.g., a power button), the buttons and the keypad can be soft keys that functionally change based on the section of the user interface currently viewed by the user, or any combination thereof.

[0040] In other examples, the programmer 106 can be a larger workstation or a separate application within another multifunctional device rather than a dedicated computing device. For example, the multifunctional device can be a notebook computer, a tablet computer, a workstation, one or more servers, a cellular phone, a personal digital assistant, or another computing device that can run an application that enables the computing device to operate as a secure medical device programmer 106. A wireless adapter coupled to the computing device can enable secure communication between the computing device and the IMD 102.

[0041] When the programmer 106 is configured for use by a clinician, the programmer 106 can be used to transmit programming information to the IMD 102. The programming information can include, for example, hardware information such as the type of lead 114, the arrangement of electrodes 116 on the lead 114, the position of the lead 114 within the brain, one or more therapy programs that define therapy parameter values, therapy windows for one or more electrodes 116, and any other information that can be programmed into the IMD 102. The programmer 106 can also be capable of performing a functional test (e.g., measuring the impedance of electrodes 116 of the lead 114) and confirming or adjusting the automatically detected FOI.

[0042] The clinician can also generate therapy programs with the aid of the programmer 106 and store them within the IMD 102. The programmer 106 can assist the clinician in creating / identifying therapy programs by providing a system for identifying potentially beneficial therapy parameter values. For example, during a programming session, the programmer 106 can automatically select a combination of electrodes for delivering therapy to the patient.

[0043] The programmer 106 can also be configured for use by the patient 104. When configured as a patient programmer, the programmer 106 may have only limited functionality (compared to a clinician programmer) to prevent the patient 104 from modifying critical functions of the IMD 102 or applications that may be harmful to the patient 104.

[0044] Regardless of whether the programmer 106 is configured for clinician use or patient use, the programmer 106 can be configured to communicate with the IMD 102 and optionally another computing device via wireless communication. For example, the programmer 106 can communicate with the IMD 102 via wireless communication using radio frequency (RF) and / or inductive telemetry techniques known in the art (which can include techniques for short-range, medium-range, or long-range communication). The programmer 106 can also communicate with another programmer or computing device via a wired or wireless connection using any of a variety of local wireless communication techniques (such as RF communication according to the 802.11 or Bluetooth specification sets, infrared (IR) communication, or other standard or proprietary telemetry protocols). The programmer 106 can also communicate with other programming or computing devices via removable media such as magnetic or optical disks, memory cards, or storage sticks. In addition, the programmer 106 can communicate with the IMD 102 and another programmer via remote telemetry techniques known in the art, for example, via a personal area network (PAN), local area network (LAN), wide area network (WAN), public switched telephone network (PSTN), or cellular telephone network.

[0045] See Figure 3B which depicts a display according to one embodiment of Figure 3ABlock diagram of the IMD 102 component in []. In an embodiment, the IMD 102 generally includes a processor 118, a memory 120, a stimulation generator 122, a sensing engine 124, a power supply 126, and a telemetry engine 128.

[0046] The processor 118 may include one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry, or a combination thereof. The functions attributed to the processors described herein may be provided by a hardware device and embodied as software, firmware, hardware, or any combination thereof. The processor 118 is configured to control the stimulation generator 122 to apply specific stimulation parameter values, such as amplitude, pulse width, and pulse frequency, specified by one or more programs, in accordance with a treatment program stored by the memory 120.

[0047] The memory 120 may be operatively coupled to the processor 118 and may include any volatile or non-volatile medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, etc. The memory 120 may store computer-readable instructions that, when executed by the processor 118, cause the IMD 102 to perform the various functions described herein.

[0048] In an embodiment, the memory 120 may store treatment programs, operation instructions, etc. Each stored treatment program defines a specific treatment program according to corresponding values of electrical stimulation parameters, such as electrode combinations, current or voltage amplitudes, and may define values of the pulse width and pulse frequency of the stimulation signal if the stimulation generator 122 generates and delivers stimulation pulses. Each stored treatment program may also be referred to as a set of stimulation parameter values. The operation instructions guide the general operation of the IMD 102 under the control of the processor 118 and may include instructions for monitoring brain signals in one or more brain regions via the electrodes 116 and delivering an electrical stimulation treatment to the patient 104.

[0049] Under the control of the processor 118, the stimulation generator 122 is configured to generate a stimulation signal for delivery to the patient 104 via a selected combination of electrodes 116.

[0050] Under the control of the processor 118, the sensing engine 124 is configured to sense the brain bioelectrical signals of the patient 104 via the electrodes 116. In some examples, the brain bioelectrical signals can reflect the current changes generated by the sum of the potential differences between brain tissues. Examples of cranial nerve signals include, but are not limited to, electrical signals generated from LFPs sensed within one or more regions of the brain, such as electroencephalogram (EEG) signals or electrocorticogram (ECoG) signals. However, the LFP can include a wider variety of electrical signals within the brain of the patient 104.

[0051] The sensing engine 124 is configured to take LFP snapshots, such as Figure 4 the LFP snapshots depicted in. The LFP snapshot represents the wide-spectrum power of the sensing channels of one or more electrodes. Detecting the change of the peak frequency over time can enable the system 100 to identify and confirm the patient-specific FOI.

[0052] The power supply 126 delivers operating power to the various components of the IMD 102. The power supply 126 can include a small rechargeable or non-rechargeable battery and a power generation circuit for generating operating power. Recharging can be achieved through close-range inductive interaction between an external charger and an inductive charging coil within the IMD 102. In some examples, the power requirement can be small enough to allow the IMD 102 to utilize patient movement and implement a kinetic energy scavenging device to trickle charge the rechargeable battery. In other examples, a conventional battery can be used for a limited time.

[0053] Under the control of the processor 118, the telemetry engine 128 is configured to support wireless communication between the IMD 102 and an external programmer 106 or another computing device. As an update to the program, the processor 118 can receive values of various stimulation parameters (such as amplitude and electrode combination) from the programmer 106 via the telemetry engine 128.

[0054] As depicted, the system 100 can also include the electrodes 116 of the lead 114, specifically including electrodes 116A to 116D. The processor 118 can apply the stimulation signals generated by the stimulation generator 122 to a selected combination of the electrodes 116A to 116D.

[0055] The embodiments of the present disclosure overcome the drawbacks of conventional DBS system LFP sensing techniques, which are limited by the programmer event tracking function entered by the user before the initial programming visit. In a conventional system, a default or arbitrary FOI is set for the patient after implantation. Then, the clinician relies on the patient to manually record one or a few programmer events (e.g., eating, taking medicine), during which an LFP snapshot is captured (i.e., recorded). This process places a significant burden on the patient when the patient has not yet received treatment and is in the postoperative recovery stage. Additionally, since the observation of the captured LFP snapshots and the modification of the FOI can only be performed during the clinician's consultation, the clinician usually has to update the FOI through several patient follow-ups until the optimal FOI is determined.

[0056] In contrast, system 100 is configured to perform automatic discovery of the FOI by generating default programmer events, obtaining timer-driven automatic snapshots of the LFP activity, and automatically adjusting the FOI based on the optimal LFP peak frequency. In an embodiment, system 100 can periodically (e.g., daily) run an electrocardiogram (ECG) to learn and extract noise from the timeline. According to an embodiment, patients with abnormal circadian rhythm patterns can be flagged for further analysis. Thus, system 100 enables the clinician to evaluate the setting effectiveness faster while reducing the number of patient follow-ups required to parse the timeline and programmer event data and verify the FOI. System 100 is also capable of achieving continuous automatic optimization of sensing based on LFP-triggered snapshots (e.g., circadian rhythm wake-up) after the clinician's visit.

[0057] System 100 can dynamically trigger the IMD 102 to record an LFP snapshot based on one or more factors such as a timer, the time of day, an accelerometer, and the observation of changes within the FOI. The observation of changes within the FOI can indicate the onset and / or dissipation of drug metabolism, whether the patient is in a sleep or awake state, or whether the patient is engaged in physical activity.

[0058] In an embodiment, determining the patient-specific FOI before initial programming can be achieved by applying a machine learning algorithm (MLA) to system 100. The embodiments of the present disclosure are operable to detect and classify the FOI peaks associated with the LFP activity without relying on conventional programmer event detection means such as instructing the user to manually record programmer events.

[0059] The inventors of the present disclosure have found that the FOI can be automatically extracted from periodic LFP snapshots through machine learning methods such as neural networks, thereby optimizing the spectrum captured by each LFP snapshot before the clinician's initial programming. This adjustment can more accurately identify the FOI before the clinician's initial programming session and confirmation.

[0060] In an embodiment, the MLA can be trained to recognize LFP snapshot triggers, particularly for changes within the FOI that indicate patient events (e.g., sleep, exercise). This recognition can be achieved by calculating a similarity metric for these patient events using correlation analysis or a machine learning regression algorithm. For example, if the similarity between the changes detected within the FOI in multiple LFP snapshots and the training data of a programmer event (such as the onset of drug metabolism) exceeds a specific threshold (e.g., 75%, 90%, 95%, or 99% similarity), the matching process can determine that the detected change represents a programmer event. These detected programmer events can be presented to the patient for confirmation and / or marked as items to be observed during a clinician's programming session.

[0061] Programmer event detection can be achieved by observing patient-specific FOI changes. For example, if a patient manually enters a "took medication" programmer event, the observed changes within the FOI can be compared to subsequent changes to recommend or identify future "took medication" programmer events that the patient did not identify.

[0062] Similarly, programmer event detection can be applied across a patient database. For example, when a specific trend is observed in an LFP snapshot, the MLA can recommend a treatment method that has been effective for other patients historically. Additionally, trends observed across patients can be utilized to resolve features indicating disease progression in LFP snapshots. Updates to the stimulation can be automatically applied based on what has been seen in other patients.

[0063] In an embodiment, the MLA can extract signal features from frequency bands to more precisely identify and confirm frequency peaks.

[0064] MLA technology can be applied to labeled (supervised) or unlabeled (unsupervised) LFP snapshot data. Additionally, the classifier can receive parameters such as the IMD model number and the type of patient programmer event. For example, frequency peaks can be separated based on patient programmer events, and image recognition technology can be used to compare each frequency peak within a programmer event. Classifying the image recognition analysis can improve detection accuracy in some cases, and the mechanisms include suppressing abnormal data interference and reducing the impact of inter-patient differences.

[0065] In operation, the sensed electrical signals can be processed by the MLA to determine context information for detected frequency peaks. In other words, the MLA can allow for the processing of patient programmer events by maintaining state information over time and prompting or automatically classifying patient programmer events based on historical patterns, time of day, or other factors. In one example, the MLA can detect a patient's sleep state based on low signals and capture a snapshot. Similarly, if multiple LFP snapshots are generated within a short time window, these snapshots can be grouped into the same programmer event. Thus, the context information surrounding the LFP snapshots may help to form personalized AI insights that can explain the wide variations in FOI among different patients.

[0066] In operation, programmer event prompts can be delivered to the external device 106. The programmer event prompts can request that the patient verify detected programmer events (e.g., eating or vigorous physical activity), and can be predefined according to the implementation. The verified classification of detected programmer events can optimize the clinician review process and simplify LFP timeline analysis.

[0067] See Figure 5 , which depicts a flowchart of a method 200 for automatically calibrating an LFP sensing tool in combination with DBS according to one implementation. The method 200 can acquire baseline brain data and implement patient-specific FOI without user input. In an implementation, the method 200 can be implemented via a DBS system (such as the system 100). In such implementations, the method 200 can run locally via the processor 118 or remotely via the server 108.

[0068] At 202, a machine learning model is trained to parse LFP snapshots and / or LFP timelines as described herein. In an implementation, the machine learning model can be trained to parse frequency peaks based on one or more of the following factors: severity, number of occurrences within a specific frequency band, time of day, patient programmer events, and information entered by the clinician or patient.

[0069] At 204, LFP sensing is enabled. In an implementation, LFP sensing is enabled in the post-anesthesia care unit (PACU) after implantation. In such implementations, default patient programmer events can be used to capture the first LFP snapshot and classify the patient's LFP peaks. For example, this default event can be labeled "medication taken".

[0070] At 206, LFP activity is monitored by automatically capturing LFP snapshots over time. In an embodiment, the LFP snapshots can be timer-driven such that the snapshots are taken periodically (e.g., every minute, every 15 minutes, every hour, every five hours). The LFP snapshots can be further captured (i.e., recorded) for each detected or patient-entered patient programmer event.

[0071] In an embodiment, the change in FOI power over time can be tracked longitudinally to create a timeline. The LFP snapshots can provide a preliminary indication of the frequencies that should be tracked, and then the timeline can be configured to plot the change in power of that particular frequency over the timeline. Then, the captured LFP snapshots are also layered onto that timeline.

[0072] A power fluctuation threshold can be combined with the timeline to determine whether the power fluctuation of an automatically selected (e.g., achieved by the peak detection method described herein) FOI is significant enough to warrant tracking the frequency peak. In other words, the power fluctuation threshold can act as a verification mechanism to ensure that the correct FOI is selected. The timeline of the change in FOI power over time can thus be utilized (or presented to the clinician) by the MLA to confirm the recommended FOI.

[0073] At 208, the new and / or aggregated data recorded by these wide-spectrum LFP snapshots allows for the automatic adjustment of the FOI timeline, thereby enabling passive and precise tracking of FOI power during the patient's home stay.

[0074] At 210, during initial programming, the clinician has the opportunity to confirm and utilize the automatically determined FOI (as long as the tracking timeline data corresponding to that FOI meets the expected requirements). Stimulation can then be applied based on the confirmed FOI.

[0075] Optionally, at 212, the machine learning model can be updated based on the FOI selected by the clinician and the trends observed in the LFP snapshot data.

[0076] It should be understood that as long as the present teachings remain operable, the various operations used in the methods of the present teachings can be performed in any order and / or simultaneously. In addition, it should be understood that as long as the present teachings remain operable, the devices and methods of the present teachings can include any number or all of the described embodiments.

[0077] Embodiments of the present disclosure are thus capable of acquiring LFP data based on the current frequency of interest (FOI) in advance. This LFP data is available to the clinician and the patient at the initial programming visit and can provide the basis for subsequent outputs including abnormal circadian rhythm detection (for subsequent follow-up by the clinician) and DBS treatment threshold adjustment.

[0078] Accordingly, the embodiments described herein lower the technical access barriers for patients and clinicians while delivering more immediate and accessible clinical value. The acquisition and conditioning of LFP data fully utilize the sensing capabilities of the neurostimulator, thus facilitating the configuration, application, and adoption of such LFP data.

[0079] The continuous acquisition of LFP data and the optimization of the FOI will further assist in the future adjustment of DBS and LFP thresholds. In an embodiment, if the LFP limit exceeds the threshold difference for more than a certain percentage of the time (e.g., more than 25%), the clinician can adjust the control strategy accordingly.

[0080] In an embodiment, the ECG can be run periodically during the exploration phase before initial programming. In such embodiments, the ECG can be run daily. Over time, the IMD processor can learn the ECG pattern and automatically extract the noise from the timeline. The additional ECG data collected before initial programming can assist the clinician in detecting and reducing ECG artifacts.

[0081] It should be understood that although the determination of the FOI has been described, the embodiments of the present disclosure can be similarly applied to automatically determine the LFP threshold and the upper and lower limits of stimulation based on the timeline data recorded during continuous DBS phases.

[0082] The embodiments of the present disclosure can be implemented on different electrode combinations to automatically update the sensing electrodes based on which electrodes have the maximum LFP activity in the FOI. The sensing electrodes can be updated when more LFP activity is recorded. Accordingly, it should be understood that the described operations can be repeated on several electrodes, enabling sequential or synchronous evaluation of various electrode combinations.

[0083] It should be understood that the various aspects disclosed herein can be combined in combinations different from those specifically presented in the specification and drawings. It should also be understood that, depending on the example, certain actions or programmer events of any of the processes or methods described herein may be performed in a different order, may be augmented, combined, or entirely omitted (e.g., not all of the described actions or programmer events may be necessary to implement these techniques). Additionally, although certain aspects of the present disclosure are described as being performed by a single engine or unit for clarity of presentation, it should be understood that the techniques of the present disclosure can be performed by a combination of units or engines associated with a medical device, for example.

[0084] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on a computer-readable medium as one or more instructions or code and executed by a hardware-based processing unit. The computer-readable medium may include a non-transitory computer-readable medium corresponding to a tangible medium, such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0085] The instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Thus, as used herein, the term “processor” may refer to any of the foregoing structures or any other physical structure suitable for implementing the described techniques. Additionally, these techniques may be fully implemented in one or more circuits or logic elements.

[0086] The following embodiments illustrate the technical solutions described herein.

[0087] Embodiment 1: A system for automatically calibrating local field potential (LFP) sensing for deep brain stimulation, comprising: one or more electrodes; a control circuit configured to, before delivering an electrical stimulus via the one or more electrodes: sense the LFP of the brain using the one or more electrodes; record a snapshot of the LFP broadband activity; determine a peak frequency of the LFP broadband activity from the snapshot; and update a frequency of interest (FOI) based on the peak frequency.

[0088] Embodiment 2: The system according to Embodiment 1, wherein the control circuit is configured to periodically record the snapshot.

[0089] Embodiment 3: The system according to Embodiment 1, wherein the control circuit is configured to record the snapshot based on a comparison of the sensed FOI with training data.

[0090] Embodiment 4: The system according to Embodiment 3, wherein the control circuit is further configured to determine that the sensed FOI represents one or more programmer events based on the comparison.

[0091] Embodiment 5: The system according to Embodiment 4, wherein at least one of the one or more programmer events is a predefined user activity corresponding to an increase or decrease in brain activity.

[0092] Example 6: The system according to Example 3 further includes an accelerometer communicatively coupled to the control circuit, wherein the control circuit is further configured to determine that the sensed FOI represents one or more programmer events based on accelerometer data received from the accelerometer.

[0093] Example 7: The system according to Example 1 further includes a user interface communicatively coupled to the control circuit, wherein the control circuit is configured to record the snapshot based on an indication of a programmer event received from the user interface.

[0094] Example 8: The system according to Example 1, wherein the control circuit is further configured to generate a timeline of the sensed power of the FOI over time.

[0095] Example 9: The system according to Example 8, wherein the control circuit is further configured to determine one or more of an LFP threshold, a stimulation upper limit, and a stimulation lower limit based on the timeline.

[0096] Example 10: The system according to Example 1, wherein the control circuit is further configured to run an electrocardiogram (ECG).

[0097] Example 11: A method for automatically calibrating local field potential (LFP) sensing for deep brain stimulation, comprising, prior to delivering an electrical stimulation: sensing an LFP of a brain using one or more electrodes; recording a snapshot of the LFP broadband activity; determining a peak frequency of the LFP broadband activity from the snapshot; and updating a frequency of interest (FOI) based on the peak frequency.

[0098] Example 12: The method according to Example 11, wherein the snapshot is recorded periodically.

[0099] Example 13: The method according to Example 11, wherein the snapshot is recorded based on a comparison of the sensed FOI with training data.

[0100] Example 14: The method according to Example 13, further comprising: determining that the sensed FOI represents one or more programmer events based on the comparison.

[0101] Example 15: The method according to Example 14, wherein at least one of the one or more programmer events is a predefined user activity corresponding to an increase or decrease in brain activity.

[0102] Example 16: The method according to Example 11, wherein the snapshot is recorded based on a user identification of a programmer event.

[0103] Example 17: The method according to Example 11 further includes: generating a timeline of the sensed power of the FOI varying over time.

[0104] Example 18: The method according to Example 17, wherein the FOI is updated based on the timeline.

[0105] Example 19: The method according to Example 17 further includes: determining one or more of an LFP threshold, a stimulation upper limit, and a stimulation lower limit based on the timeline.

[0106] Example 20: The method according to Example 11 further includes: running an electrocardiogram (ECG).

Claims

1. A system for automatically calibrating local field potential (LFP) sensing for deep brain stimulation, comprising: One or more electrodes; A control circuit configured to, before delivering electrical stimulation via the one or more electrodes: Sense the LFP of the brain using the one or more electrodes; Record a snapshot of the LFP broadband activity; Determine the peak frequency of the LFP broadband activity from the snapshot; And Update the frequency of interest (FOI) based on the peak frequency.

2. The system according to claim 1, wherein the control circuit is configured to periodically record the snapshot.

3. The system according to claim 1, wherein the control circuit is configured to record the snapshot based on a comparison of the sensed FOI with training data.

4. The system according to claim 3, wherein the control circuit is further configured to determine that the sensed FOI represents one or more programmer events based on the comparison.

5. The system according to claim 4, wherein at least one of the one or more programmer events is a predefined user activity corresponding to an increase or decrease in brain activity.

6. The system according to claim 3, further comprising an accelerometer communicatively coupled to the control circuit, wherein the control circuit is further configured to determine that the sensed FOI represents one or more programmer events based on accelerometer data received from the accelerometer.

7. The system according to claim 1, further comprising a user interface communicatively coupled to the control circuit, wherein the control circuit is configured to record the snapshot based on an indication of a programmer event received from the user interface.

8. The system according to claim 1, wherein the control circuit is further configured to generate a timeline of the sensed power of the FOI over time.

9. The system according to claim 8, wherein the control circuit is further configured to determine one or more of an LFP threshold, a stimulation upper limit, and a stimulation lower limit based on the timeline.

10. The system according to claim 1, wherein the control circuit is further configured to run an electrocardiogram (ECG).

11. A method for automatically calibrating local field potential (LFP) sensing for deep brain stimulation, comprising, before delivering electrical stimulation: Sense the LFP of the brain using one or more electrodes; Record a snapshot of the LFP broadband activity; Determine the peak frequency of the LFP broadband activity from the snapshot; And Update the frequency of interest (FOI) based on the peak frequency.

12. The method according to claim 11, wherein the snapshot is periodically recorded.

13. The method according to claim 11, wherein the snapshot is recorded based on a comparison of the sensed FOI with training data.

14. The method according to claim 13, further comprising: Determine that the sensed FOI represents one or more programmer events based on the comparison.

15. The method according to claim 14, wherein at least one of the one or more programmer events is a predefined user activity corresponding to an increase or decrease in brain activity.

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