Neurological disease adaptive treatment device and method for controlling the neurological disease adaptive treatment device

Abstract

The present disclosure relates generally to the field of neurological disease treatment, and in particular to devices and methods for treating neurological diseases based on adaptive stimulation, and methods for controlling such devices. In particular, the present disclosure relates generally to a neurological disease adaptive treatment device (10) including an implantable electrode (11) configured to sense neural activity signals and apply electrical stimulation signals, and a processing and stimulation unit (14) connected to the implantable electrode (11). The processing and stimulation unit includes at least a stimulation module (16) configured to generate a stimulation signal delivered by the implantable electrode (11), the stimulation signal characterized by at least one stimulation parameter, an acquisition module (20) configured to record a neural activity signal record of the neural activity signal sensed by the implantable electrode, and a processing module (18). The processing module (18) is configured to process the neural activity signal recordings recorded by the acquisition module (20) based on a first control logic (22) and adjust at least one stimulation parameter (A) based on the at least one neural activity signal recording processed according to the first control logic (22), the first control logic being a function dependent on at least one signal feature (Fi) of the neural activity signal recordings and composed of a plurality of different function elements based on at least one control parameter (Cj), each function element of the plurality of different function elements being associated with a respective range of the at least one signal feature (Fi), and the processing module (18) is configured to process the neural activity signal recordings recorded by the acquisition module (20) based on a second control logic (23) and adjust the at least one control parameter (Cj) of the first control logic (22) based on the at least one neural activity signal recording processed according to the second control logic (23).

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates generally to the field of neurological treatment, and more particularly to adaptive stimulation based neurological treatment devices and methods of controlling such devices. [Background technology]

[0002] Invasive electrical stimulation is nowadays a treatment option for various neurological disorders, including movement disorders, psychiatric disorders, and pain. Examples of invasive brain stimulation devices include deep brain stimulation (DBS) systems, which are primarily used to treat Parkinson's disease, dystonia, essential tremor, epilepsy, and obsessive-compulsive disorder, and spinal cord stimulators (SCS) for pain management. Deep brain stimulation (DBS) systems offer many advantages and are used in various industries, including medical diagnosis and treatment.

[0003] For example, deep brain stimulation can provide electrical stimulation to neural structures in a patient's central nervous system to modulate neural activity. Conventional deep brain stimulation is often programmed by a physician for predefined stimulation settings, which are kept constant over time. Considering that stimulation usually consists of a rectangular-shaped train of electrical pulses with amplitude, pulse width and pulse frequency, the stimulation settings set the value of at least one of the above-mentioned parameters (stimulation amplitude, stimulation pulse width and stimulation frequency). However, neurological diseases are progressive and symptoms often fluctuate over time. Therefore, addressing neurological diseases, managing symptoms and correctly titrating treatment is not a simple task, as it requires objective and repeated assessment of the patient's clinical condition. For example, a patient's pain sensation cannot be measured quantitatively, but only by qualitative measures and evaluations. Moreover, pain sensation changes over time.

[0004] Such patients would benefit from adaptive, patient-specific stimulation that compensates for symptom fluctuations by adjusting stimulation settings in real time based on neurophysiological control variables. However, the effectiveness of adaptive techniques is strictly linked to the stability of the neurophysiological signal, which may be influenced by the degradation and relative position of the electrode-tissue interface as well as the underlying pathophysiological mechanisms. In conclusion, adaptive techniques are suitable for managing symptom fluctuations but cannot address disease progression or signal degradation over time.

[0005] Due to this limitation, maintaining the robustness and effectiveness of adaptive control over time remains a challenge for known adaptive deep brain stimulation devices. The same is true for spinal cord stimulators. Thus, there is a need for new and improved devices and methods of controlling such devices for the adaptive treatment of neurological disorders. Summary of the Invention

[0006] The applicant set out to overcome the above problems, in particular to eliminate the need for initialization and long-term adjustments of the device for adaptive treatment of neurological disorders in order to set up and maintain long-term patient-tailored therapy that provides optimized stimulation.

[0007] Within the scope of the above problem, the applicant aimed to devise an apparatus that can stimulate neural tissue in accordance with a control logic, collect neural signals generated in response to the stimulation, adjust stimulation parameters based on the collected neural signals, and adjust the control logic.

[0008] Thus, a first aspect of the present invention relates to an apparatus for the adaptive treatment of a neurological disorder, the apparatus comprising: an implantable electrode configured to sense neural activity signals and apply electrical stimulation signals; a processing and stimulation unit connected to the implanted electrodes, the processing and stimulation unit comprising at least: a stimulation module configured to generate a stimulation signal delivered by the implanted electrode, the stimulation signal being characterized by at least one stimulation parameter; an acquisition module configured to record a neural activity signal recording of the neural activity signal sensed by the implanted electrode; and a processing module configured to process neural activity signal recordings recorded by the acquisition module based on a first control logic and to adjust at least one stimulation parameter based on the at least one neural activity signal recording processed according to the first control logic, the first control logic being a function dependent on at least one signal feature of the neural activity signal recordings and based on the at least one control parameter and being comprised of a plurality of different function elements, each function element of the plurality of different function elements being associated with a respective range of the at least one signal feature; Here, the processing module is configured to process the neural activity signal recordings recorded by the acquisition module based on a second control logic, and adjust at least one control parameter of the first control logic based on the at least one neural activity signal recording processed according to the second control logic.

[0009] In this specification and the appended claims, the expression "function made of a plurality of different function pieces" is intended to refer to a piecewise function, i.e. a function defined by a plurality of partial functions, each of which is a different function element applied to a different interval within the domain.

[0010] The applicant has discovered that by iteratively adjusting the control parameters characterizing the first control logic based on the acquired neural activity signals, it is possible for the deep brain stimulation device to both self-initialize and self-tune when initiating treatment, and to provide robust, long-term optimized stimulation.

[0011] Applicant has noted that while fluctuations in a patient's condition occur in real time (events within a day), disease progression and changes at the electrode-tissue interface occur over longer time frames (days, weeks, months), and therefore adjustments to the control parameters of the first control logic need to be made based on the slower fluctuation trends experienced by the neural activity signal as opposed to the real-time fluctuations used to adjust the stimulation parameters.

[0012] Applicant also noted that control logic aimed at driving stimulation parameters in real time based on static control parameters cannot adequately meet the requirements for long-term robustness. Applicant therefore realized that adjustable control parameters are needed to account for long-term signal changes associated with changes in the electrode-tissue interface and / or disease progression.

[0013] All of the above applies in particular in the case of control logic based on piecewise functions (functions consisting of multiple different functional elements), i.e. functions defined by multiple sub-functions, each sub-function being applied to a different interval in the domain, as known in the art. As known in the art, the different domain intervals of a piecewise control logic applied to the adaptive treatment of a neurological disease are selected based on and strongly depend on the patient's condition in the absence and presence of drug therapy and / or stimulation.

[0014] The applicant has noted that such conditions are not constant over time, but rather may change over longer periods of time (days, weeks, months), and therefore the selected sectional control logic is not optimal for real-time setting of stimulation parameters.

[0015] A second aspect of the invention relates to a method of controlling an apparatus for adaptive treatment of a neurological disorder, the method comprising the steps of: processing the neural activity signal recording recorded by an acquisition module of the device for adaptive treatment of a neurological disorder based on a first control logic, the first control logic being a function dependent on at least one signal feature of the neural activity signal recording, based on at least one control parameter and composed of a plurality of different function elements, each function element of the plurality of different function elements being associated with a respective range of the at least one signal feature; adjusting at least one stimulation parameter of the stimulation signal based on the at least one neural activity signal recording processed according to the first control logic; processing the neural activity signal recording recorded by the acquisition module based on a second control logic; and Adjusting at least one control parameter of the first control logic based on the at least one neural activity signal recording processed according to the second control logic.

[0016] Advantageously, the method for controlling a device for adaptive treatment of neurological disorders achieves the same advantages as those described with reference to the device for adaptive treatment of neurological disorders according to the invention.

[0017] The present invention may have at least one of the following preferred features, the latter of which may in particular be combined with one another as desired to meet specific implementation needs.

[0018] In general, in some variations, the processing and stimulation unit may be further configured to define a stimulation parameter window comprised between at least a minimum value of the stimulation parameter Amin (amplitude, pulse width, frequency, or a combination thereof) that produces a detectable clinical benefit in the patient and a maximum stimulation parameter Amax (amplitude, pulse width, frequency, or a combination thereof) before producing an adverse effect in the patient. In some embodiments, the stimulation parameter window (Amax, Amin) may be entered as a non-adjustable parameter.

[0019] Advantageously, setting stimulation parameter windows instead of specific stimulation parameter values ​​facilitates and accelerates the initialization phase performed by the physician.

[0020] In some variations, at least one stimulation parameter of the stimulation signal is adjusted based on a time series of collected neural activity signal recordings that is shorter than the time series of collected neural activity signal recordings for which the at least one control parameter of the first control logic is adjusted.

[0021] In this way, it becomes possible to compensate for disease progression and electrode-tissue interface changes that occur over a much longer time frame (days, weeks or months) compared to patient symptom fluctuations that occur over a period of less than a day. Thus, by adjusting the control parameters of the first control logic according to the second control logic that takes into account the longer time series of collected neural activity signal recordings, it is possible to take into account long-term signal changes associated with electrode-tissue interface changes and / or disease progression.

[0022] In some variations, at least one function element of the plurality of function elements of the first control logic may be a function dependent on at least one signal feature Fi (i=1,...,N) of the neural activity signal recording. At least one function element of the plurality of function elements may be a linear function of at least one signal feature Fi of the neural activity signal recording.

[0023] The first control logic may include a first function in which the stimulation parameter A is proportional to the signal feature F with respect to a of a first timeseries F1 of the collected neural activity signal features. The first function may be related to a first range (C2 < F1 < C1) of the first collected timeseries of neural activity signal features. The first control logic may include a second function that defines an upper limit value Amax of the stimulation parameter A. The second function may be related to a second range (F1 > C1) of the signal feature amount. The first control logic may further include a third function that defines a lower limit value Amin of the stimulation parameter A. The third function may be related to a third range (F1 < C2) of the signal feature. The upper and lower limits of the stimulation parameter A can define a stimulation parameter window (Amax, Amin).

[0024] The first control logic can be as follows:

Number

[0025] In this case, the first control logic is characterized by a first control parameter C1 and a second control parameter C2, and the first control parameter C1 and the second control parameter C2 define a first range for the first timeseries F1 of the collected neural activity signal features and can be adjusted based on the second control logic, particularly based on a second timeseries F2 of the collected neural activity signal features.

[0026] In some embodiments, the processing and stimulation unit of the implantable device may further be configured to extract spectral features within a frequency band of the neural activity signal recordings made over a predetermined period. The frequency band may be a low-frequency band, an alpha frequency band, or a beta frequency band and a gamma frequency. The frequency band may be determined by identifying a peak of the extracted spectral features and defining the frequency band as a frequency range around the peak, preferably centered on the peak. In some variations, the frequency band may be preset (hard-coded) or input as a non-adjustable parameter.

[0027] In some embodiments, at least one signal characteristic of the neural activity signal recordings may be the spectral characteristic of the neural activity signal recordings within the frequency band, preferably the spectral power of the neural activity signal recordings within the frequency band.

[0028] Accordingly, the first control logic may comprise a first function in which the stimulation parameter A is proportional to the power P of the signal in the frequency band. The first function may be related to a first power range (Pmin < P < Pmax). The first control logic may comprise a second function that defines an upper limit Amax of the stimulation parameter A. The second function may be related to a second power range (P > Pmax). The first control logic may further comprise a third function that defines a lower limit Amin of the stimulation parameter A. The third function may be related to a third power range (P < Pmin).

[0029] Accordingly, the first control logic may be as follows:

Equation

[0030] In this case, the first control logic is characterized by a first control parameter corresponding to a maximum spectral power Pmax and a second control parameter corresponding to a minimum spectral power, the first and second control parameters being adjustable based on the second control logic, in particular based on a time series of the power P of the signal in the frequency band that is different from the time series in which the stimulation parameter A is adjusted.

[0031] In some embodiments, the first control parameter Pmax can be initialized by setting it equal to the power of a frequency band of the spectrum of the neural activity signal, preferably measured in the absence of medication, and the second control parameter Pmin can be initialized by setting it equal to the power of a frequency band of the background activity spectrum fitted to the spectrum of the neural activity signal under any medication or stimulation condition.

[0032] The background activity spectrum is calculated by multiplying the spectrum of the neural activity signal by the following noise function:

number

[0033] In some variations, the second control logic may be implemented as a Bollinger Bands calculation, where the first control parameter Pmax is set equal to the band upper limit and the second control parameter Pmin is set equal to the band lower limit. Both the band upper limit and the band lower limit may be calculated based on a power time series collected over minutes, hours, days, weeks, or months. The Bollinger Bands calculation may be based on a simple moving average for any period, such as 30 minutes, 1 hour, 1 day, 1 week, etc. The Bollinger Bands may also be calculated based on an exponential moving average. The upper and lower limits of the Bollinger Bands may be a number k of standard deviations (positive or negative) from the moving average.

[0034] In a preferred embodiment, the average power in a frequency band TIFF2025502410000005.tif7165 and the mean standard deviation TIFF2025502410000006.tif7165 can be calculated by sliding the time period.

[0035] In a preferred embodiment, the upper and lower limits of the Bollinger Bands are calculated as follows:

number

number

[0036] Advantageously, the Bollinger Bands calculation provides a time-relative setting of the first and second control parameters Pmax and Pmin, thereby enabling self-adjustment of the first control logic.

[0037] In some embodiments, the second control logic may be an unsupervised learning model that establishes clusters of neural activity signal recordings based on at least one signal feature. The unsupervised learning model may be implemented as a K-means clustering method. The K-means clustering method may assign signal records to K clusters based on the distance of the signal feature from the centroid of each cluster, where K is a hyperparameter corresponding to the total number of clusters.

[0038] In some embodiments, the at least one signal feature of the neural activity signal records may be the spectral power of a frequency band calculated for each record of the neural activity signal, and the number of clusters may be two with control parameters Pmax and Pmin corresponding to the centroids of each cluster.

[0039] In some variations, the unsupervised learning model can be implemented as an exclusive (e.g., K-means), overlapping (e.g., fuzzy K-means), hierarchical or probabilistic (e.g., Gaussian mixture model) clustering model. The number of clusters is either predefined (e.g., 2 clusters) or set during and / or after acquisition of the neural activity signal recordings.

[0040] In some embodiments, the second control logic may be a time-based fuzzy controller that estimates a new set of control parameters for the first control logic based on at least one signal feature of the neural activity signal recordings and an associated time slot of a day during which the neural activity signal recordings were obtained. The time-based fuzzy controller can group and process at least one signal feature of the recorded neural activity signals based on the time slot during which each neural activity signal was recorded.

[0041] In some embodiments, the at least one signal feature of the neural activity signal recordings may be a spectral power of a frequency band calculated for each recording of the neural activity signal, and the control parameters Pmax and Pmin may be equal to the average value of the spectral power values ​​of first and second groups of spectral power values, each group of spectral power values ​​including spectral power values ​​for the neural activity signal recorded during a respective time period of a day. [Brief description of the drawings]

[0042] [Figure 1] FIG. 1 is a schematic diagram of an exemplary apparatus for adaptive treatment of neurological disorders according to a preferred embodiment of the present invention; [Diagram 2] FIG. 2 is a block diagram of the closed-loop control logic implemented by the method of controlling an apparatus for adaptive treatment of neurological disorders according to a preferred embodiment of the present invention; [Figure 3A] FIG. 3A is a block diagram of an exemplary embodiment of a second control logic implemented by an adaptive deep brain stimulation device in accordance with the present invention; [Figure 3B]FIG. 3B is a block diagram of an exemplary embodiment of a second control logic implemented by an adaptive deep brain stimulation device in accordance with the present invention; [Figure 3C] FIG. 3C is a block diagram of an exemplary embodiment of a second control logic implemented by an adaptive deep brain stimulation device in accordance with the present invention; [Figure 4A] FIG. 4A is a flowchart of a method for controlling an apparatus for adaptive treatment of a neurological disorder according to a first embodiment of the present invention; [Figure 4B] FIG. 4B is a flowchart of a method for controlling an apparatus for adaptive treatment of a neurological disorder according to a second embodiment of the present invention; [Diagram 5] FIG. 5 is a flow chart of an initialization routine of a method for controlling an apparatus for adaptive treatment of a neurological disorder according to the present invention; [Figure 6] Figure 6 is a graph of the spectrum of the neural signal and background activity; [Figure 7] FIG. 7 is a schematic diagram of an exemplary apparatus and associated patient personal controller device for adaptive treatment of a neurological disorder. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0043] In the figures and the following description, the same reference numerals or symbols are used to indicate components having the same functions. Moreover, for clarity of illustration, some reference numerals may not be repeated in all figures. Although embodiments and variants of the present invention have been depicted and described herein, it should be understood that there is no intention to limit the present invention to the specific embodiments and variants described below, but on the contrary, the present invention is intended to cover all modifications or alternatives and equivalent embodiments falling within the scope of protection of the present invention as defined in the claims.

[0044] Words such as "example," "e.g.," "or," and the like, indicate non-exclusive options without limitation, unless expressly indicated otherwise. Words such as "comprises" and "including" have the meaning "comprising or including, but not limited to," unless expressly indicated otherwise. Additionally, a "module" as referred to throughout may refer to an electrical circuit and / or an assembly of electrical components arranged and connected to perform one or more functions described herein, and / or may refer to a special purpose computer that is programmed to perform the functions described herein.

[0045] Referring to FIG. 1, one variation of an apparatus for adaptively treating a neurological disorder is shown and generally designated 10.

[0046] In particular, the device shown in FIG. 1 is suitable for adaptive deep brain stimulation configured to detect biopotentials (e.g., local field potentials, or LFPs) from the stimulating electrode or adjacent electrodes and to correlate such signals to stimulation effects and / or adapt stimulation parameters to facilitate patient treatment.

[0047] The adaptive treatment device 10 for neurological disorders includes at least one probe or electrical catheter 11 configured to be implanted in the patient's brain to provide electrical stimulation. The probe or electrical catheter 11 may include at least three metal contacts or leads accessible via external connections, also referred to as electrodes 12. However, in other variations, the electrodes may not be located on the same electrical catheter (e.g., the adaptive DBS device may include two or more electrical catheters and the electrodes may be located on two different electrical catheters).

[0048] The device 10 for adaptive treatment of neurological disorders can include one or more implantable probes, each of which can include one or more electrodes. The device 10 can also include a connector or probe extension for each of the implantable probes. The probe (e.g., probe 11) can have a distal portion and a proximal portion. One or more electrodes (for providing electrical stimulation and / or acquiring neural activity data) are disposed on the distal portion, one or more connector contacts are disposed on the proximal portion, and one or more wires in the probe electrically connect the electrodes and the connector contacts. The probe 11 can be configured with any number of electrodes 12, e.g., 1, 2, 3, 4, 5, 6, 8, 10, 12, 16, 24, 36, 48, 64, 96, etc., and a corresponding number of connector contacts. The probe extension may have a distal portion having a connector block with a receptacle housing that encloses one or more conductive contacts, a proximal portion having stimulator (e.g., device 10) connector contacts, each of which corresponds to a conductive contact in the receptacle housing via one or more wires, and an elongated body between the proximal and distal portions. The probe extension may be configured with any number of conductive contacts, e.g., 1, 2, 3, 4, 5, 6, 8, 10, 12, 16, 24, 36, 48, 64, 96, etc., and a corresponding number of stimulator connector contacts. The number of conductive contacts in the probe extension may be the same as or greater than the number of electrodes on the probe to which the probe extension is connected. The distal portion of the probe is implantable in a target brain region, and the proximal portion of the probe extends outside the brain tissue and can be connected to the distal portion of the probe extension. The receptacle housing of the probe extension can be configured to hold the proximal portion of the probe such that the connector contacts of the probe electrically connect with the conductive contacts of the probe extension and the electrodes of the distal portion of the probe electrically couple with the stimulator connector contacts of the proximal portion of the probe extension. The stimulator connector contacts can be configured to be coupled to a port or connector (e.g., a header interface) of the processing and stimulation unit 14.In some variations, the receptacle housing may include an attachment mechanism for engaging or retaining the proximal portion of the probe within the receptacle housing. Optionally, the probe extension may include a connector sleeve or boot of electrically insulating material disposed over at least a portion of the receptacle housing to help electrically insulate the connector contacts of the probe and the conductive contacts of the probe extension from surrounding tissue. The elongated body of the probe extension may have a constant diameter between the distal and proximal portions or may have a diameter that varies along its length. For example, the diameter of a segment of the elongated body may be larger (e.g., thicker) if the segment is intended to be located at the interface between brain tissue and the skull or skin. This may reduce excessive twisting, torque, and / or bending of the wires within the elongated body of the probe extension, reducing mechanical wear on the wires and / or increasing the usable life of the probe extension.

[0049] Although the device 10 for adaptive treatment of neurological disorders depicted in FIG. 1 includes a probe 11 having four metal contacts or electrodes 12, other variations of the probe may include any number of electrodes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 16, 18, 20, 25, 30, 36, 48, or more). As previously mentioned, the device 10 for adaptive treatment of neurological disorders may include any number of probes (e.g., two or more), where each probe may have any number of electrodes. For example, the device 10 for adaptive treatment of neurological disorders may include a first probe having a first electrode and a second probe having a second electrode. In use, the first probe may be implanted in a first brain region and the second probe may be implanted in a second brain region (e.g., for bilateral stimulation). In another variation, the device 10 for adaptive treatment of a neurological disorder may include two probes, each of which may have four electrodes (a total of eight channels) or each of which may have eight electrodes (a total of sixteen channels).

[0050] In one variation, the probe 11 may include a plurality of electrodes, where a first electrode is a stimulating electrode for providing electrical stimulation and a second electrode is a measuring electrode for acquiring neural activity signals. For example, a first plurality of electrodes (which may or may not be adjacent to each other) may be used for stimulation, and a second plurality of electrodes (which may or may not be adjacent to each other, or may be interleaved with the first plurality of electrodes) may be used for acquiring neural activity signals. Alternatively or additionally, the same electrode(s) may be used for both acquiring neural activity signals and for electrical stimulation, either simultaneously or sequentially. The DBS probe may be comprised of one or more cylindrical or disc-shaped electrodes having a height of about 0.5 mm to about 3 mm, e.g., about 1.5 mm, and a diameter of about 0.5 mm to about 2 mm, e.g., about 1.27 mm. In some variations, the DBS probe may include two or more cylindrical electrodes (e.g., using 2, 4, 6, 10, 12, 15, 16, 20, etc. electrodes). Alternatively or additionally, the DBS probe may include a planar electrode and / or a sharp electrode having a shape selected at least in part based on the target neural structure or brain region. The spacing between the two electrodes may be about 0.25 mm to about 2 mm, for example about 0.5 mm, and optionally, an insulator may be disposed between and / or around the two electrodes to reduce electrical coupling or crosstalk between the electrodes. The insulator may include, for example, polyurethane and / or polyimide, etc. The electrodes may be made of any metal or any metal alloy, for example, a platinum-iridium alloy.

[0051] In the embodiment shown in FIG. 1, the electrodes 12 are connected to a processing and stimulation unit 14, which includes three functional modules connected to each other in a feedback and interoperation configuration: a stimulation module 16, a data acquisition module 20 and a processing module 18. The processing and stimulation unit 14 can be entirely included in the implantable part 10a of the device 10 for adaptive treatment of neurological disorders, or it can be distributed between the implantable part 10a and the external part 10b. In this second case, raw and / or processed neural activity signal recordings are wirelessly transmitted from the implantable part 10a to the external part 10b and vice versa. The external part 10b can be implemented as a patient personal controller device. The implantable part 10a and the external part 10b of the device 10 for adaptive treatment of neurological disorders are described in detail below and are depicted diagrammatically in FIG. 7.

[0052] In one variation, the processing and stimulation unit 14 may include 16 channels, which may be connected to two probes with eight electrodes each, or four probes with four electrodes each, or eight probes with two electrodes each, etc. There may be fewer electrodes than channels, e.g., the processing and stimulation unit 14 may be configured to accommodate 16 channels (e.g., 16 stimulating and / or LFP acquisition electrodes, etc.), but a particular embodiment of the device 10 or DBS system for adaptive treatment of a neurological disorder may include eight electrodes (e.g., two probes with four electrodes) or four electrodes (e.g., a single probe with four electrodes).

[0053] The stimulation module 16 is employed to generate and transmit stimulation signals to the electrodes 12. The stimulation module 16 may include a pulse or function generator including a voltage and / or current source and circuitry configured to generate electrical pulses having specific parameter values ​​determined by a user and / or a controller, and may also include wires that transmit the electrical pulses to a probe that delivers the electrical pulses to a brain region.

[0054] In some variations, the stimulation module may include a waveform generator (e.g., pulse generator, function generator), a current controller, and a multiplexer, one or more of which may be configured to receive command signals from the processing module 18. The command signals may include electrical stimulation parameter data, including, but not limited to, stimulation amplitude, pulse width, pulse frequency, duty cycle, and / or the particular probe(s) and / or electrode(s) to which electrical stimulation with the specified parameters should be delivered. The current controller may be configured to set the electrical stimulation amplitude specified by the command signal, and / or the waveform generator may be configured to generate current or voltage pulses having a pulse width and / or pulse frequency specified by the command signal. The multiplexer may be configured to electrically connect the probes and / or electrodes specified by the command signal with the current controller and / or the waveform generator. In some variations, the multiplexer may include a multiplexer array configured according to command signals from the main processor such that electrical pulses from the waveform generator are directed to selected probes and / or electrodes. The connections between the waveform generator and the electrodes can be arranged by a multiplexer in a monopolar and / or bipolar stimulation configuration. In a monopolar configuration, one or more electrodes may be connected to one or more active (e.g., positive) terminals of the waveform generator (with return pads located elsewhere on the patient). In a bipolar configuration, a first set of one or more electrodes may be connected to one or more active (e.g., positive) terminals of the waveform generator, while a second set of one or more electrodes (e.g., different from the first set of electrodes) may be connected to one or more return (e.g., negative) terminals of the waveform generator.

[0055] In some variations, the stimulation module 16 may be configured to generate a stimulation signal that may be characterized by a set of parameters and transmit the stimulation signal to one or more of the electrodes 12. For example, the stimulation module 16 may include a pulse generator having a current source (and / or a voltage source) that generates an electrical signal having parameters specified by a user and / or a processing module. In some variations, the pulse generator can form output pulses having a specified amplitude, frequency and / or pulse width or duration value. Optionally, the pulse generator may generate a pulse sequence having two pulses or multiple pulses repeated at a duty cycle specified by the user and / or the processing module 18, and the processing module 18 may adjust the pulse duty cycle according to one or more characteristics of the acquired neural activity signal (e.g., any of the patterns or characteristics described herein).

[0056] The data acquisition module 20 is responsible for acquiring signals representative of cerebral activity coming from the patient's brain, for example LFP signals that may be representative of cerebral activity in the brain region in which the probe 11 is implanted. The acquisition module 20 is in electrical communication with the probe 11, which may in a variant be the same probe used to electrically stimulate the brain region. The acquisition module 20 and / or the probe 11 may be configured to acquire neural activity signals, such as localized potentials (LFPs) resulting from activity in the brain region proximate to the probe 11. The acquisition module 20 may comprise an acquisition processor and a memory for storing and analyzing the acquired neural activity signals.

[0057] The processing module 18 performs adaptive control of the stimulation module 16 based on signals acquired by the acquisition module 20. The processing module may have circuitry configured to facilitate communication between the acquisition module 20 and the stimulation module 16, coordinate signaling between the acquisition module 20 and the stimulation module 16, and / or perform additional calculations on the acquired neural activity signals.

[0058] The processing module 18 may be part of either the acquisition module or the stimulation module, or may be a separate module. In some variations, the processing module comprises circuitry configured to regulate / adjust the operation of the stimulation module based on a signal from the acquisition module (e.g., based on an LFP signal indicative of neural activity). The processing module may have a module (main) processor and memory that analyzes and stores the acquired neural activity signal and / or the signal from the acquisition module. In some variations, the processing module may comprise circuitry that regulates the power provided to the stimulation module, for example, in coordination with the electrical stimulation parameters determined by the acquisition module and / or the acquired neural activity signal. The characteristics or parameters of the electrical stimulation may be determined by the acquisition module and / or the processing module. For example, the processor of the acquisition module and / or the processing module may analyze the acquired and / or stored neural activity signal to identify fluctuations or changes in the pattern or characteristics of the neural activity signal. The processing module may provide command signals to a pulse generator of the stimulation module to change parameters of the electrical stimulation in response to changes in the neural activity signal detected or extracted by the acquisition module. The processing module may also include a battery (e.g., a rechargeable battery) and circuitry configured to charge and / or measure the charge remaining in the battery. For example, the processing module may include a rechargeable battery, an inductive link for charging the battery, and an inductive coil for facilitating energy transfer between an external charging device and the stimulator (which may be implanted in the patient). Optionally, the processing module may include a wireless transmission interface (e.g., a transceiver) including an RF chip and an RF antenna for signal transmission between the implantable stimulator and an external device.In some variations, the acquisition module may include a processor configured to calculate a spectral power value of the acquired neural activity signal, the calculated power value may be transmitted to the processing module, and the processor of the processing module may be configured to derive stimulation parameters according to the power value and transmit a general command signal to the pulse generator to adapt or adjust parameters of the electrical stimulation. Optionally, the processing module may include additional sub-modules having circuitry configured for power management, electrode impedance checks, and / or calibration and / or diagnostic analysis (e.g., troubleshooting) of the stimulation module.

[0059] Returning to the acquisition module 20 of FIG. 1, its main function is to measure the electric field variations of the local biopotentials by directly sensing the difference between the potentials referenced to the common electrode 17, and to amplify such difference to reach a voltage level useful for the analog-to-digital conversion required for signal processing.

[0060] Thus, the acquisition module 20 may comprise input ports each connected to a different electrode 12 on the probe 11, and electrical circuitry configured to measure local biopotentials or electric field variations of localized potentials (LFPs) based on signals from the input ports. The electrical circuitry of the acquisition module may comprise one or more processing units or processors (e.g., a CPU, and / or one or more field programmable gate arrays, and / or one or more application specific integrated circuits) that may be configured to perform computational operations, one or more memory elements, one or more amplifiers, one or more filters, and / or one or more analog-to-digital converters.

[0061] 2 is a functional block diagram illustrating operations performed by the acquisition module 20 and the processing module 18 of the processing and stimulation unit 14. As previously mentioned, in block 21, the acquisition module 20 can calculate a set of signal features, e.g., spectral power values, of the acquired neural activity signal. The spectral power values ​​may be transmitted to the processing module 18, and the processing module processor may apply a first control logic (shown in block 22) to the received spectral power values ​​to derive stimulation parameters for adapting or adjusting parameters of the electrical stimulation.

[0062] The first control logic of block 22 may be a function dependent on at least one signal feature Fi (i=1,...,N) of the neural activity signal recording and may be based on at least one control parameter Cj (j=1,...,M). The first control logic may be composed of a plurality of different functions, each function element of the plurality of different function elements being associated with a respective range of the at least one signal feature Fi. At least one function element of the plurality of function elements may be a function dependent on the at least one signal feature Fi of the neural activity signal recording. At least one function element of the plurality of function elements may be a linear function of the at least one signal feature Fi of the neural activity signal recording. At least one further function element of the plurality of function elements may be a constant value.

[0063] For example, in one embodiment, the first control logic can include a first function where the stimulation parameter A is proportional to the signal feature F with respect to a of the first time series F1 of the collected neural activity signal features. The first function may be related to a first range (C2 < F1 < C1) of the first collected time series of neural activity signal features. The first control logic may include a second function that defines an upper limit value Amax of the stimulation parameter A. The second function may be related to a second range of the signal features (F1 > C1). The first control logic may further include a third function that defines a lower limit value Amin of the stimulation parameter A. The third function may be related to a third range of the signal features (F1 < C2). The upper and lower limits of the stimulation parameter A can define a stimulation parameter window (Amax, Amin).

[0064] Thus, the first control logic can be as follows: [Number]

[0065] In this case, the first control logic defines a first range for the first time series F1 of the collected neural activity signal features and can be characterized by a first control parameter C1 and a second control parameter C2 that are adjusted based on the second control logic, particularly based on a second time series F2 of the collected neural activity signal feature quantities processed according to the second control logic.

[0066] For example, in one embodiment, at least one signal feature of the neural activity signal recording may be a spectral feature within a frequency band, preferably the power of the neural activity signal within the frequency band. The frequency band may be a low-frequency band, an alpha frequency band, or a beta frequency band and a gamma frequency. The frequency band may be preset (hard-coded) or input as a non-adjustable parameter.

[0067] Accordingly, the first control logic may include a first function in which the stimulation parameter A is proportional to the power of the signal in the frequency band. The first function may be related to a first power range (Pmin < P < Pmax). The first control logic may include a second function that defines an upper limit value Amax of the stimulation parameter A. The second function may be related to a second power range (P > Pmax). The first control logic may further include a third function that defines a lower limit value Amin of the stimulation parameter A. The third function is related to a third power range (P < Pmin).

[0068] In this implementation, the first control logic can be as follows:

Number

[0069] In this case, the first control logic is characterized by a first control parameter corresponding to the maximum spectral power Pmax and a second control parameter corresponding to the minimum spectral power Pmin.

[0070] In some implementations, at least one signal feature can be one of, or any combination of, signal amplitude, signal phase, entropy, coherence between or within signals, amplitude coupling between or within phases, fractal spectrum, fractal dimension, phase locking value, modulation index, kurtosis, variability index, etc.

[0071] The processing module can be further configured to process the neural activity signal record received from the acquisition module based on a second control logic (shown in block 23) and adjust at least one of the control parameters Pmin, Pmax of the first control logic in block 22 based on the neural activity signal record processed according to the second control logic.

[0072] 3A to 3C are examples of second control logic 23 that the processing module 18 can use to adjust the control parameters Pmin, Pmax that characterize the first control logic.

[0073] In the example of Figure 3A, the second control logic is implemented as a Bollinger band calculation, where a first control parameter Pmax is set equal to an upper band limit and a second control parameter Pmin is set equal to a lower band limit, both of which may be calculated based on power time series collected over minutes, hours, days, weeks, or months.

[0074] Bollinger Bands can be calculated based on a simple moving average of any period: 30 minutes, 1 hour, 1 day, 1 week, etc. Bollinger Bands can also be calculated based on an exponential moving average. The upper and lower limits of the Bollinger Bands can be the number k of standard deviations (positive or negative) from the moving average.

[0075] Average power in a frequency band TIFF2025502410000011.tif7165 and the mean standard deviation TIFF2025502410000012.tif7165 may be calculated by the second control logic over sliding time periods, for example, 24 hours. Thus, after 24 hours, for each time step (e.g., every minute), the processing module can calculate the upper and lower limits of the Bollinger Bands based on the data collected during the past 24 hours.

[0076] The upper and lower limits of the Bollinger Bands can be calculated as follows:

number

number

[0077] The calculation period of the moving average and standard deviation and the width of the Bollinger Bands, given by the coefficient k, are the hyperparameters of the Bollinger Bands calculation.

[0078] In some variations, the Bollinger band operation may be applied to a dynamically adjustable control parameter of a first control logic that defines a threshold. The first control logic may control switching the stimulation on and off based on a threshold of at least one signal feature of the neural activity signal recording. The threshold may be calculated as an upper or lower limit of the Bollinger band.

[0079] In the example of Fig. 3B, the second control logic 23 can be implemented as an unsupervised learning model that establishes clusters of neural activity signal recordings based on at least one signal feature. The unsupervised learning model can be implemented as a K-means clustering method. The K-means clustering method can assign signal recordings to K clusters based on the distance of the signal feature from the centroid of each cluster, where K is a hyperparameter corresponding to the total number of clusters.

[0080] As an example, the at least one signal feature of the neural activity signal recording may be the spectral power of the frequency bands calculated for each recording of the neural activity signal, and the number of clusters may be two with control parameters Pmax and Pmin corresponding to the centroid of each cluster. Thus, the calculated spectral powers that are close to the centroid of the cluster are classified into the corresponding cluster. The value of each centroid is updated after assigning the calculated spectral power to each cluster. The control parameters Pmax and Pmin determined in the initialization step provide the starting values ​​of the cluster centroids and are iteratively changed according to the calculated spectral power values ​​added to the respective clusters. This allows the control parameters Pmax and Pmin to be adjusted over time based on the evolution of the recorded neural activity signal. Alternatively, the centroid values ​​can be randomly initialized.

[0081] In some variations, the unsupervised learning model can be implemented as an exclusive (e.g., k-means), overlapping (e.g., fuzzy k-means), hierarchical or probabilistic (e.g., Gaussian mixture model) clustering model. The number of clusters is predefined (e.g., 2 clusters) or set during and / or after acquisition of the neural activity signal recordings.

[0082] In the example of Fig. 3C, the second control logic 23 may be implemented as a time-based fuzzy controller that estimates a new set of control parameters of the first control logic based on at least one signal feature of the neural activity signal recording and the associated time of day of the recording. The time-based fuzzy controller may group and process at least one signal feature of the recorded neural activity signals based on the time of day when the respective neural activity signal was recorded.

[0083] For example, in one embodiment, signal features of neural activity signals recorded between 10:00 AM to 12:00 PM and 2:00 PM to 4:00 PM may be grouped to belong to a first group, and signal features of neural activity signals recorded between 8:00 AM to 10:00 AM and 12:00 PM to 2:00 PM may be grouped to belong to a second group. As an example, at least one signal feature of the neural activity signal recording may be a spectral power of a frequency band calculated for each recording of the neural activity signal. The control parameters Pmax and Pmin may be equal to the average value of the spectral power values ​​of the first group and the second group, respectively. In a real-time application scenario, the control parameters Pmax and Pmin may be updated daily, weekly, or monthly. The time periods of the day for grouping the signal features of the neural activity signals may be preset.

[0084] FIG. 4A is a first exemplary method 100 for controlling a device for adaptive treatment of neurological disorders according to the present invention. The method 100 comprises, at 101, acquiring and / or storing neural activity signal recordings, e.g., localized potentials (LFPs), for a predefined period (e.g., 1 day, 10 days, etc.). For example, in some embodiments, neural activity signal recordings can be acquired during the day (e.g., in the course of daily life), and recorded separately for the left subthalamic nucleus (STN) and the right STN in a frequency range between 5 Hz and 35 Hz. The acquisition can be performed at a frequency band of the neural activity signal recordings selected from a low frequency band, an alpha frequency band, a beta frequency band, and / or a gamma frequency band. The frequency band can be determined by identifying a peak in the extracted spectral features and defining the frequency band as a frequency range around the peak, preferably centered on the peak. In some variations, the frequency band is preset (hard-coded) or input as a non-adjustable parameter.

[0085] Method 100 comprises processing the acquired neural activity signal recordings and extracting neural signal features, at 102. For example, in one embodiment, the method can provide for extraction of spectral features, e.g., spectral power, within frequency bands of the neural activity signal recordings recorded during a predefined time period.

[0086] The extracted signal features may be transmitted at 103 to the processing module 18 for deriving stimulation parameters according to the first and second control logics, as described above. In particular, at least one stimulation parameter of the stimulation signal is adjusted based on the first control logic and the neural activity signal recordings, while the neural activity signal recordings are processed according to the second control logic to adjust the control parameters of the first control logic.

[0087] At least one stimulation parameter of the stimulation signal is adjusted based on a time series of collected neural activity signal recordings, which time series is typically shorter than the time series of collected neural activity signal recordings for which the control parameter of the first control logic is adjusted.

[0088] 4B is a second exemplary method 200 for controlling an apparatus for adaptive treatment of a neurological disorder in accordance with the present invention. In addition to the steps of the first exemplary method 100, method 200 comprises a routine for initializing a first control logic based on collected neural signals.

[0089] The method 200 comprises the steps of acquiring and / or storing neural activity signal recordings at 101 and further processing the acquired neural activity signal recordings and extracting neural signal features at 102. If the process has just started and the first control logic still needs to be initialized, i.e. its control parameters still need to be set, the steps of acquiring and / or storing neural activity signal recordings and further processing the acquired neural activity signal recordings are preferably performed in the absence of stimulation and / or medication, and in 201 and 202 the method uses the neural signal features extracted from the acquired neural activity signal recordings to calculate the control parameters (described in more detail below). Finally, adaptive stimulation is initiated at 203.

[0090] 5 is an exemplary method 300 for initializing a first control logic, particularly when the first control logic is a function dependent on the spectral power in a frequency band of a neural activity signal, and the control parameters of the first control logic are a first control parameter corresponding to a minimum spectral power Pmin and a second control parameter corresponding to a maximum spectral power Pmax. The method 300 provides for processing a neural activity signal recording obtained in the absence of stimulation. The neural activity signal recording is processed 301 to extract a signal spectrum 601 (shown in FIG. 6) and determine a peak frequency 603 of the neural activity signal recording. Based on the location of the peak within the frequency range, a patient-specific frequency band is selected and set around, preferably centered on, the peak.

[0091] At 302, the method 300 determines the spectral power 605 of the neural activity signal recording in a selected patient-specific frequency band. The method then determines 303 the spectral power 604 of the background activity spectrum 602 in the same patient-specific frequency band. To this end, a noise function is fitted to the frequency spectrum of the neural activity signal recording. The noise function is

number

[0092] Finally, the first and second control parameters Pmax and Pmin are set at 304 and 305, respectively. The first control parameter Pmax is set equal to the spectral power 605 of the neural activity signal recording acquired in the absence of stimulation, calculated in the selected patient-specific frequency band, and the second control parameter Pmin is set equal to the spectral power 604 of the background activity spectrum, calculated in the selected patient-specific frequency band, preferably fitted to the spectrum of the neural activity signal acquired in the absence of stimulation and / or medication.

[0093] FIG. 7 is a schematic diagram of an exemplary method for establishing a wireless connection between the implantable portion 10a (also referred to herein as an “implantable pulse generator (IPG) device”) and the external portion 10b (also referred to herein as a “patient personal controller device”) of the neurological disease adaptive treatment device 10. The patient personal controller device 10b can transmit power via the wireless connection to charge the IPG 10a. Alternatively or additionally, the IPG device 10a can transmit / receive neural activity signal recordings and / or stimulation parameters to / from the patient personal controller device 10b. For example, the IPG 10a can transmit neural activity signal recordings to the patient personal controller device 10b via the wireless connection, and the personal controller device 10b can transmit stimulation parameters or instructions to the IPG 10a via the wireless connection. In some cases, the wireless connection between the implantable device 10a and the patient personal controller device 10b (e.g., a charger unit of the patient personal controller device) may be established when the patient personal controller device 10b and the implantable device 10a are within a predetermined distance range (e.g., 2 centimeters to 10 centimeters, and / or 1 millimeter to 1 meter, etc.) and orientation range. The orientation range may include, for example, a 5 rotational degree error range, a 10 rotational degree error range, and / or an alignment of the vertical orientation of the patient personal controller device with the vertical orientation of the implantable device within such range. The personal controller device 10b can transmit power to the implantable device 10a, and the implantable device 10a can transmit neural activity signal recordings to the personal controller device 10b via the first wireless connection.

[0094] In the above description, for convenience of explanation, particular reference is made to the application of DBS. However, the disclosed invention is also applicable to different embodiments, such as SCS for pain treatment. In the case of SCS, the acquisition module can be configured to acquire neural activity of the spinal nerves in response to electrical stimulation when electrodes are placed on the spine. The response of the spinal cord to the electrical stimulation is the summation of activation of an ensemble of nerve fibers, i.e., Evoked Compound Action Potentials (ECAPs). The processing module can be configured to calculate a set of signal features of the acquired neural activity signal (e.g., the spectral power of the ECAPs). The spectral power values ​​may be transmitted to the processing module, and the processor of the processing module may apply a first control logic to the received signal features to derive stimulation parameters for adapting or adjusting parameters of the electrical stimulation. The processing module processor can also apply a second control logic to adjust the control parameters of the first control logic, as described above. The above considerations regarding the implementation of the first control logic and the second control logic still apply.

[0095] In the foregoing description, for ease of explanation, specific nomenclature has been used to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that specific details are not required to practice the present invention. Thus, the foregoing descriptions of specific variations of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed, and obviously, many modifications and variations are possible in light of the above teachings. The variations have been selected and described in order to explain the principles of the invention and its practical application, so that one skilled in the art can utilize the present invention and various variations with various modifications as suited to the particular use contemplated. It is intended that the following claims and their equivalents define the scope of the present invention.

Claims

1. A device (10) for the adaptive treatment of a neurological disorder, comprising: an implantable electrode (11) configured to sense neural activity signals and apply electrical stimulation signals; a processing and stimulation unit (14) connected to said implanted electrodes (11); The processing and stimulation unit comprises: a stimulation module (16) configured to generate a stimulation signal delivered by the implanted electrode (11), the stimulation signal being characterized by at least one stimulation parameter; an acquisition module (20) configured to record neural activity signal recordings of neural activity signals sensed by the implanted electrodes; a processing module (18) configured to process the neural activity signal recordings recorded by the acquisition module (20) based on a first control logic (22) and to adjust the at least one stimulation parameter (A) based on the at least one neural activity signal recording processed according to the first control logic (22), the first control logic being a function dependent on at least one signal feature (Fi) of the neural activity signal recordings and based on at least one control parameter (Cj) and consisting of a plurality of different function elements, each function element of the plurality of different function elements being associated with a respective range of the at least one signal feature (Fi); the processing module (18) configured to process the neural activity signal recordings recorded by the acquisition module (20) based on a second control logic (23) and to adjust the at least one control parameter (Cj) of the first control logic (22) based on the at least one neural activity signal recording processed according to the second control logic (23); At least Apparatus (10).

2. At least one stimulation parameter (A) of the stimulation signal is determined based on a first time series (F １ ) and at least one control parameter (Cj) of the first control logic (22) is adjusted based on a second time series (F ２ ) and the first time series is shorter than the second time series.

2. The apparatus (10) of claim 1.

3. The second time series of collected neural activity signal recordings on which the adjustment of at least one control parameter (Cj) of the first control logic (22) is based is a spectral power time series collected over minutes, hours, days, weeks, or months.

3. The apparatus (10) of claim 2.

4. The second control logic (23) is a Bollinger Bands calculator and is controlled by the first control parameter (C １ ) is set to the upper band limit, and the second control parameter (C ２ ) is set to the lower band limit, or the second control logic (23) is an unsupervised learning model that establishes, for each control parameter (Cj) of the first control logic (22), a cluster of the neural activity signal recordings based on at least one signal feature (Fi) thereof, each control parameter (Cj) corresponding to a centroid of a respective cluster; or The second control logic (23) is a time-based fuzzy controller that estimates at least one control parameter (Cj) of the first control logic (22) based on the at least one signal feature of the neural activity signal recording and the associated time of day when the neural activity signal recording was obtained. An apparatus (10) according to any one of claims 1 to 3.

5. At least one function element of the plurality of function elements of the first control logic (22) is a function that depends on the at least one signal feature (Fi) of the neural activity signal recording, and is preferably a linear function of the at least one signal feature (Fi) of the neural activity signal recording. An apparatus (10) according to any one of claims 1 to 4.

6. The first control logic: The at least one stimulation parameter (A) is a first function proportional to the at least one signal characteristic (Fi), the first function being proportional to a first range (C ２ <Fi<C １ ), and A second function defining an upper limit (Amax) of the at least one stimulation parameter (A), the second function being within a second range (Fi>C) of the at least one signal characteristic. １ ), and a second function related to a third function defining a lower limit (Amin) of the at least one stimulation parameter (A), the third function being a function of a third range (Fi<C ２ and a third function related to An apparatus (10) according to any one of the preceding claims.

7. The first control logic (22) [0010] The first range of the first time series of collected neural activity signal features of the signal features is determined by the second time series of collected neural activity signal features (F ２ ) which is a control parameter of the first control logic (22) adjusted according to the second control logic (23) based on １ ) and lower limit (C ２ ) 7. The apparatus (10) according to claim 6.

8. The processing and stimulation unit (14) is configured to extract spectral features within a frequency band of the neural activity signal recording recorded during a predefined period of time, the frequency band being preferably a low frequency band, an alpha frequency band, a beta frequency band or a gamma frequency band; and The at least one signal feature of the neural activity signal recording is a spectral feature of the neural activity signal recording within the frequency band, preferably the spectral power of the neural activity signal recording within the frequency band. An apparatus (10) according to any one of the preceding claims.

9. The first control logic (22) includes a first function in which the at least one stimulation parameter (A) is proportional to a spectral power (P) of the neural activity signal recording within the frequency band, the first function being associated with a first power range (Pmin<P<Pmax) comprised between a minimum spectral power (Pmin) and a maximum spectral power (Pmax).

9. The apparatus (10) according to claim 8.

10. The first control logic (22) includes a second function defining an upper limit (Amax) of the at least one stimulation parameter (A), the second function being associated with a second power range (P>Pmax) adjacent to the first power range (Pmin<P<Pmax), and a third function defining a lower limit (Amin) of the at least one stimulation parameter (A), the third function being associated with a third power range (P<Pmin) adjacent to the first power range (Pmin<P<Pmax).

10. The apparatus (10) according to claim 9.

11. The first control logic (22) [0025] where Amin and Amax are lower and upper limits, respectively, of the stimulation parameter (A), and the minimum spectral power (Pmin) and the maximum spectral power (Pmax) are control parameters of the first control logic (22) adjusted based on the at least one neural activity signal recording processed according to the second control logic (23).

11. Apparatus (10) according to claim 9 or 10.

12. The upper band limit and the lower band limit are average power The average standard deviation away from wherein the average power and the average standard deviation are both calculated over a sliding time period, and preferably the average power is calculated as a simple moving average of the spectral power of the neural activity signal recording within the frequency band, or as an exponential moving average of the spectral power of the neural activity signal recording within the frequency band. An apparatus (10) according to any one of claims 8 to 11 when claim 4 is recited.

13. The at least one control parameter (Cj) of the first control logic (22) is set equal to the average value of the signal features grouped by the second control logic (23) based on the time of day when the neural activity signal recordings were acquired. An apparatus (10) according to any one of claims 4 to 10.

14. The processing module (18) is further configured to initialize the at least one control parameter (Cj) based on at least one neural activity signal recording acquired by the acquisition module (20). An apparatus (10) according to any one of claims 1 to 13.

15. The processing and stimulation unit (14) is configured to define a stimulation parameter window comprised between a lower limit value (Amin) of the at least one stimulation parameter (A) and an upper limit value (Amax) of the at least one stimulation parameter (A). An apparatus (10) according to any one of claims 1 to 14.

16. A method (100) for controlling a device (10) for adaptive treatment of a neurological disorder, comprising: - processing neural activity signal recordings recorded by an acquisition module (20) of said device (10) for adaptive treatment of a neurological disorder based on a first control logic (22), said first control logic (22) being a function dependent on at least one signal feature (Fi) of said neural activity signal recordings and based on at least one control parameter (Cj) and being composed of a plurality of different function elements, each function element of said plurality of different function elements being associated with a respective range of said at least one signal feature (Fi); adjusting at least one stimulation parameter (A) of a stimulation signal based on at least one neural activity signal recording processed according to said first control logic (22); processing the neural activity signal recordings recorded by the acquisition module (20) based on a second control logic (23); adjusting said at least one control parameter (Cj) of said first control logic (22) based on at least one neural activity signal recording processed according to said second control logic (23); A method (100).

17. At least one stimulation parameter (A) of the stimulation signal is adjusted based on a first time series of collected neural activity signal records, and the at least one control parameter (Cj) of the first control logic (22) is adjusted based on a second time series of collected neural activity signal records, the first time series being shorter than the second time series.

20. The method (100) of claim 16.

18. the second time series of collected neural activity signal recordings on which the adjustment of the at least one control parameter (Cj) of the first control logic (22) is based is a power time series collected over minutes, hours, days, weeks or months; 20. The method (100) of claim 17.

19. The second control logic (23) is a Bollinger band calculator and is a first control parameter (C １ ) is set equal to the band upper limit, and a second control parameter (C ２ ) is set equal to the lower band limit, or the second control logic (23) is an unsupervised learning model that establishes, for each control parameter (Cj) of the first control logic (22), a cluster of the neural activity signal recordings based on at least one signal feature (Fi) thereof, each control parameter (Cj) corresponding to a centroid of a respective cluster; or The second control logic (23) is a time-based fuzzy controller that estimates at least one control parameter (Cj) of the first control logic (22) based on the at least one signal feature of the neural activity signal recording and the associated time of day when the neural activity signal recording was obtained.

19. The method (100) of any one of claims 16 to 18.

20. The step of processing the neural activity signal recording comprises extracting spectral features within a frequency band of the neural activity signal recording recorded during a predefined period of time, the frequency band being preferably a low frequency band, an alpha frequency band, a beta frequency band or a gamma frequency band; and At least one signal feature (Fi) of the neural activity signal recording is a spectral feature of the neural activity signal recording within the frequency band, preferably the spectral power of the neural activity signal recording within the frequency band.

20. The method (100) of any one of claims 16 to 19.

21. The upper band limit and the lower band limit are average power The average standard deviation away from wherein the average power and the average standard deviation are both calculated over a sliding time period, and preferably the average power is calculated as a simple moving average of the spectral power of the neural activity signal recording within the frequency band, or as an exponential moving average of the spectral power of the neural activity signal recording within the frequency band.

21. The method (100) of claim 19 or 20.

22. The at least one control parameter (Cj) of the first control logic (22) is set equal to the average value of the signal features grouped by the second control logic (23) based on the time of day when the neural activity signal recordings were acquired.

22. The method (100) of any one of claims 19 to 21.

23. and initializing said at least one control parameter (Cj) based on at least one neural activity signal recording acquired by said acquisition module (20).

23. The method (100) of any one of claims 16 to 22.

24. The first control logic (22) defines a stimulation parameter window that is included between a lower limit value (Amin) of the at least one stimulation parameter (A) and an upper limit value (Amax) of the at least one stimulation parameter (A).

24. The method (100) of any one of claims 16 to 23.

25. At least one function element of the plurality of function elements of the first control logic (22) is a function dependent on the at least one signal feature (Fi) of the neural activity signal recording, preferably a linear function of the at least one signal feature (Fi) of the neural activity signal recording.

25. The method (100) of any one of claims 16 to 24.

26. The first control logic: The at least one stimulation parameter (A) is a first function proportional to the at least one signal characteristic (Fi), the first function being proportional to a first range (C ２ <Fi<C １ ) a first function related to A second function defining an upper limit (Amax) of the at least one stimulation parameter (A), the second function being within a second range (Fi>C) of the at least one signal characteristic. １ ) a second function related to a third function defining a lower limit (Amin) of the at least one stimulation parameter (A), the third function being within a third range (Fi<C ２ and a third function related to 26. The method (100) of any one of claims 16 to 25.

27. The first control logic (22) [0030] and a first time series of collected neural activity signal features of said signal features (F １ ) is a second time series of neural activity signal features (F) collected according to the second control logic (23). ２ ) is a control parameter of the first control logic (22) adjusted based on １ ) and lower limit (C ２ ) 27. The method (100) of claim 26.

28. The first control logic (22) includes a first function in which the at least one stimulation parameter (A) is proportional to a spectral power (P) of the neural activity signal recording within the frequency band, the first function being associated with a first power range (Pmin<P<Pmax) comprised between a minimum spectral power (Pmin) and a maximum spectral power (Pmax). The method (100) according to any one of claims 20 to 27.

29. The first control logic (22) includes a second function defining an upper limit (Amax) of the at least one stimulation parameter (A), the second function being associated with a second power range (P>Pmax) adjacent to the first power range (Pmin<P<Pmax), and a third function defining a lower limit (Amin) of the at least one stimulation parameter (A), the third function being associated with a third power range (P<Pmin) adjacent to the first power range (Pmin<P<Pmax).

30. The method (100) of claim 28.

30. The first control logic (22) [0045] where Amin and Amax are lower and upper limits, respectively, of the stimulation parameter (A), and the minimum spectral power (Pmin) and the maximum spectral power (Pmax) are first and second control parameters of the first control logic (22) adjusted based on the at least one neural activity signal recording processed according to the second control logic (23).

30. The method (100) of claim 28 or 29.

31. The first control parameter (Pmax) is initialized by setting it equal to the power in the frequency band of the spectrum of the measured neural activity signal, preferably in the absence of a drug, and the second control parameter (Pmin) is initialized by setting it equal to the power in the frequency band of the background activity spectrum fitted to the spectrum of the neural activity signal under any drug or stimulation condition.

31. The method (100) of claim 30.

32. The fitting of the background activity spectrum is performed using a noise function [0050] (α is equal to 0, 1 or 2) or by calculating the fractal component of the neural activity signal.

32. The method (100) of claim 31.

Images