Device for adaptive treatment of nervous system diseases and method for initializing device for adaptive treatment of nervous system diseases

Abstract

The present disclosure relates generally to the field of nervous system disease treatment, and in particular to devices for treating nervous system diseases based on adaptive stimulation and methods for controlling such devices. Specifically, the present disclosure generally relates to a device (10) for adaptive treatment of nervous system diseases, comprising: an implantable electrode (11) configured to sense a neural activity signal and apply an electrical stimulation signal; and a processing and stimulation unit (14) connected to the implantable electrode (11), where the processing and stimulation unit comprises at least: a stimulation module (16) configured to generate a stimulation signal to be carried on the implantable electrode (11), the stimulation signal being characterized by at least one stimulation parameter; an acquisition module (20) configured to record a neural activity signal record of a neural activity signal sensed by the implantable electrode; and a processing module (18) configured to process the neural activity signal record recorded by the acquisition module (20) based on control logic (22), which is a function dependent on at least one signal characteristic (Fi) of the neural activity signal record, and to tune the at least one stimulation parameter (A) based on the at least one neural activity signal record processed according to the control logic (22), on the basis of the at least one control parameter (Cj) and consisting of a plurality of function segments, each function segment of the plurality of function segments relating to a respective range of the at least one signal characteristic (Fi), the processing module (18) is further configured to process the neural activity signal record recorded by the acquisition module (20) based on the initialization logic (23, 23 ') and / or to receive at least one neural activity signal record processed according to the initialization logic (23, 23'), and initializing at least one control parameter (Cj) of the control logic (22) based on the at least one neural activity signal record processed according to the initialization logic (23); and wherein, the initialization logic (23, 23 ') is configured to extract at least one signal feature (Fi) from the neural activity signal record, and determining a distribution of the at least one extracted signal feature (Fi) based on the number of times the extracted signal feature (Fi) substantially reaches a given value of the plurality of given values or is included within a given range of the plurality of given ranges.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to the field of nervous system disease treatment, and in particular to a device for treating nervous system diseases based on adaptive stimulation and a method for controlling such a device. BACKGROUND

[0002] Invasive electrical stimulation is currently a treatment option for a variety of nervous system diseases including movement disorders, psychiatric disorders, and pain. Examples of invasive brain stimulation devices are deep brain stimulation (DBS) systems, primarily used for the treatment of Parkinson’s disease, dystonia, essential tremor, epilepsy, and obsessive-compulsive disorder, and spinal cord stimulators (SCS) for pain management. Deep brain stimulation (DBS) systems are used in multiple industries including medical diagnostics or medical treatments due to many advantages.

[0003] For example, deep brain stimulation can provide electrical stimulation to neural structures of a patient’s central nervous system to modulate neural activity. Conventional deep brain stimulation is typically programmed by a physician for a predefined stimulation setting that remains constant over time. Considering that stimulation typically consists of a series of square electrical pulses with an amplitude, pulse width, and pulse frequency, the stimulation setting sets the value of at least one of the above parameters (stimulation amplitude, stimulation pulse width, and stimulation frequency). However, nervous system diseases are progressive, and symptomology often fluctuates over time. Therefore, managing symptoms and correctly titrating treatment for nervous system disorders is not a simple task as it requires objectively and repeatedly assessing the patient’s clinical state. For example, a patient’s pain sensation cannot be measured quantitatively, but only qualitatively by means of scales and assessments. Moreover, pain sensation can vary over time.

[0004] Such patients benefit from adaptive and 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 tightly related to the statistical properties of neurophysiological signals, which can be affected by electrode-tissue interface degradation and pathophysiological mechanisms of relative position and potential evolution. In summary, while adaptive techniques are suitable for managing symptom fluctuations, programming can be trivial due to the statistical properties of neurophysiological signals. End-users, including physicians or medical technicians, can not have the background knowledge to select the optimal programming parameters to optimize the benefit of adaptive stimulation devices.

[0005] Due to this limitation, in known devices for adaptive treatment of nervous system diseases, achieving robustness and effectiveness of adaptive control remains a pursuit target. Similar considerations apply to spinal cord stimulators. Therefore, there is a need for new and improved devices for adaptive treatment of nervous system diseases and methods for controlling such devices. SUMMARY

[0006] The applicant considered overcoming the aforementioned problems, and specifically addressing issues related to the initialization and long-term tuning of devices for adaptive therapy of neurological disorders, in order to set up and maintain the patented customized therapy over time, thereby providing optimized stimulation.

[0007] Within the scope of the aforementioned problems, the applicant has considered the following objective: to design a device capable of stimulating neural tissue according to control logic, collecting neural signals generated in response to the stimulation, and adjusting stimulation parameters and tuning control logic based on the collected neural signals.

[0008] Therefore, a first aspect of the present invention relates to an apparatus for adaptive treatment of nervous system diseases, comprising: Implantable electrodes are configured to sense neural activity signals and apply electrical stimulation signals. A processing and stimulation unit connected to an implantable electrode, wherein the processing and stimulation unit includes at least: The stimulation module is configured to generate a stimulation signal to be carried at the implanted electrode, the stimulation signal being characterized by at least one stimulation parameter. The acquisition module is configured to record neural activity signals sensed by implanted electrodes; and The processing module is configured to process neural activity signal recordings from the acquisition module based on control logic, and to tune at least one stimulation parameter based on at least one neural activity signal recording processed according to the control logic. The control logic is a function that depends on at least one signal feature of the neural activity signal recording, is based on at least one control parameter, and consists of multiple function segments, wherein each of the multiple function segments is associated with a corresponding range of at least one signal feature.

[0009] According to the present invention, the processing module is further configured to process neural activity signal records recorded by the acquisition module based on initialization logic and / or receive at least one neural activity signal record processed according to the initialization logic, and to initialize at least one control parameter of the control logic based on at least one neural activity signal record processed according to the initialization logic.

[0010] Furthermore, the initialization logic is configured to extract at least one signal feature from the neural activity signal recording, and to determine the distribution of at least one extracted signal feature based on the number of times the extracted signal feature substantially reaches one of a plurality of given values ​​or the number of times it is included in one of a plurality of given ranges.

[0011] The applicant discovered that initializing the control parameters representing the control logic based on the collected neural activity signals and specifically based on the distribution of the extracted features enables robust and persistent optimized stimulation.

[0012] The applicant observed that while fluctuations in patient symptoms occurred in real time (within a day or less), disease progression and changes at the electrode-tissue interface occurred over a much longer timeframe (days, weeks, or months). Therefore, the initial settings of the control parameters for the control logic needed to be based on trends in neural activity signals over a longer timeframe (days, weeks, or months), rather than on real-time changes used to adjust stimulation parameters.

[0013] A second aspect of the invention relates to a method for controlling an apparatus for adaptive treatment of nervous system diseases, comprising the following steps: The control logic processes neural activity signal recordings obtained by the acquisition module of a device for adaptive treatment of neurological disorders. The control logic is a function dependent on at least one signal feature of the neural activity signal recordings, based on at least one control parameter, and consists of multiple function segments, each of which is associated with a corresponding range of the at least one signal feature. At least one stimulation parameter is adapted to the stimulation signal based on at least one neural activity signal record processed according to control logic. At least one neural activity signal record is processed based on initialization logic to extract at least one signal feature from the neural activity signal record. F i And based on the extracted signal features F i Essentially, the number of times a given value is reached among a plurality of given values, or the number of times a signal is included within a plurality of given ranges, determines at least one extracted signal feature. F i The distribution, and At least one control parameter of the control logic is initialized based on at least one neural activity signal record processed by the initialization logic.

[0014] Advantageously, the method of controlling the device for adaptive treatment of nervous system diseases achieves the same advantages as described for the device for adaptive treatment of nervous system diseases according to the invention.

[0015] Another aspect of the present invention relates to a system for adaptive treatment of neurological disorders, comprising: an apparatus for adaptive treatment of neurological disorders as described above; and a clinician programmer device configured to be connected to the apparatus for adaptive treatment of neurological disorders to receive neural activity signal recordings, process the received neural activity signal recordings based on initialization logic, and transmit at least one neural activity signal recording processed according to the initialization logic to a processing module of the apparatus for adaptive treatment of neurological disorders.

[0016] Advantageously, the system for controlling the adaptive therapy device for nervous system diseases achieves the same advantages as described for the device for adaptive therapy of nervous system diseases according to the invention.

[0017] The present invention may have at least one of the following preferred features; specifically, the following preferred features may be combined as needed to meet specific implementation requirements.

[0018] Typically, in some variations, the initialization logic is configured to initialize the first control parameter of at least one control parameter of the control logic. C The initial value of 1 is chosen to be higher than the value of the extracted signal feature that appears most frequently, and the second control parameter in at least one control parameter of the control logic is... C The initial value of 2 is chosen to be lower than the value of the extracted signal feature that appears most frequently.

[0019] Alternatively or additionally, the initialization logic is configured to initialize a second control parameter among at least one control parameter of the control logic. C The initial value of 2 is set to be equal to the k1th percentile of the distribution, where k1 is equal to or less than 50, and the first control parameter in at least one control parameter of the control logic is set to... C The initial value of 1 is set to be equal to the k2th percentile, where k2 is equal to or greater than 50.

[0020] In this specification and the appended claims, the expression "the kth percentile of a distribution" means that a given percentage k in the distribution falls on or below that value. For example, the 25th percentile of a distribution means that 25% of the distribution's scores fall below that value.

[0021] Preferably, at least one of the extracted signal features has a normal distribution, and the initialization logic is configured to set a first control parameter among at least one control parameter of the control logic. C 1 and second control parameters C The initial value of 2 is chosen as:

[0022] Where k = {1, ..., 5}, σ is the mean of the distribution and σ is the standard deviation of the distribution.

[0023] According to an alternative embodiment, at least one of the extracted signal features has a first peak at higher signal feature values ​​and a second peak at lower signal feature values; and wherein the initialization logic is configured to set the first control parameter of at least one control parameter of the control logic. C The initial value of 1 is set to be equal to the higher signal characteristic value, and the second control parameter in at least one control parameter of the control logic is... C The initial value of 2 is set to be equal to the lower signal characteristic value.

[0024] Alternatively, the initialization logic is configured to initialize a second control parameter from at least one control parameter of the control logic. C The initial value of 2 is set to be equal to the lower signal characteristic value, and the first control parameter in at least one control parameter of the control logic is set to... C The initial value of 1 is set to be equal to or higher than the highest signal characteristic value.

[0025] Preferably, the distribution of at least one extracted signal feature is based on a time series of neural signal activity recordings lasting for hours, days, weeks, or months.

[0026] Typically, in some variations, the processing and stimulation unit can also be configured to define a stimulation parameter window that includes between: a minimum value of at least one stimulation parameter Amin (amplitude, pulse width, frequency, or a combination thereof) that elicits a detectable clinical benefit in the patient and a maximum stimulation parameter Amax (amplitude, pulse width, frequency, or a combination thereof) that elicits a side effect in the patient. In some embodiments, the stimulation parameter window (Amax, Amin) can be used as a non-tunable parameter input.

[0027] Advantageously, setting a stimulation parameter window instead of specific stimulation parameter values ​​helps and speeds up the initialization phase performed by doctors or medical technicians.

[0028] In some variations, the initialization logic is initialization and adjustment logic, and the processing module is also configured to process neural activity signal recordings recorded by the acquisition module based on the initialization and adjustment logic, and to tune at least one control parameter of the control logic based on at least one neural activity signal recording processed according to the initialization and adjustment logic.

[0029] In some variations, at least one stimulation parameter of the stimulation signal is tuned based on a time series of collected neural activity signal recordings, wherein the time series of collected neural activity signal recordings is shorter than the time series of collected neural activity signal recordings on which at least one control parameter of the tuning control logic is based.

[0030] In this way, disease progression and changes at the electrode-tissue interface can be compensated for over a much longer timeframe (days, weeks, or months) than the fluctuations in a patient's symptoms (which occur within a range of less than a day). Therefore, tuning the control parameters of the control logic according to the initialization and adjustment logic (which takes into account a longer time series of collected neural activity signal recordings) can account for long-term signal changes associated with electrode-tissue interface alterations and / or disease progression.

[0031] In some variations, at least one of the multiple function segments of the control logic may be at least one signal feature dependent on the recording of neural activity signals. F i ( i =1、……、 N The function is a sequence of multiple function segments. At least one function segment can be at least one signal feature of a neural activity signal recording. F i A linear function.

[0032] The control logic may include a first function segment, in which the stimulus parameter A and signal characteristics are... F Proportional. The first function segment can be proportional to the first range of signal characteristics ( C 2< F < C 1) Correlation. The control logic may include a second function segment that defines an upper limit Amax of the stimulus parameter A. The second function segment may be correlated with a second range of signal characteristics ( F > C 1) Related. The control logic may also include a third function segment that defines the lower limit Amin of the stimulus parameter A. The third function segment may be related to a third range of signal characteristics ( F < C 2) Relevance. The upper and lower limits of the stimulus parameter A can be used to define the stimulus parameter window (Amax, Amin).

[0033] The control logic can be as follows:

[0034] In this case, the control logic is determined by the first control parameter. C 1 and second control parameters C2. Characterization: The first and second control parameters can be adjusted based on initialization and adjustment logic.

[0035] In some implementations, the processing and stimulation unit of the implantable device can also be configured to extract spectral features within a frequency band of neural activity signals recorded during a predefined time period. The frequency band can be a low-frequency band, an alpha band, or a beta and gamma band. The frequency band can be determined by identifying peaks in the extracted spectral features and defining the frequency band as a range of frequencies surrounding those peaks (preferably centered on those peaks). In some variations, the frequency band can be preset (hard-coded) or input as a non-tunable parameter.

[0036] In some implementations, at least one signal feature of the neural activity signal recording can be the spectral feature of the neural activity signal recording within a frequency band, preferably the spectral power of the neural activity signal recording within a frequency band.

[0037] Therefore, the control logic may include a first function segment in which the stimulus parameter A is proportional to the power P of the signal in the frequency band. The first function segment may be related to a first power range (…). Pmin < P < Pmax The control logic may include a second function segment that defines an upper limit Amax for the stimulus parameter A. This second function segment may be related to a second power range (…). P > Pmax The control logic may also include a third function segment that defines the lower limit Amin of the stimulus parameter A. This third function segment may be related to a third power range (…). P < Pmin (Related to)

[0038] Therefore, the control logic can be as follows:

[0039] In this case, the 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. The first and second control parameters can be adjusted based on initialization and adjustment logic.

[0040] In a preferred embodiment, the initialization and adjustment control logic can be implemented as Bollinger Band calculation, wherein a first control parameter Pmax is set equal to the upper Bollinger Band and a second control parameter Pmin is set equal to the lower Bollinger Band. Both the upper and lower Bollinger Bands can be calculated based on power time series collected over minutes, hours, days, weeks, or months. The Bollinger Band calculation can be based on a simple moving average over any time period (e.g., 30 minutes, one hour, one day, one week, etc.). Alternatively, the Bollinger Bands can be calculated based on an exponential moving average. The upper and lower Bollinger Bands can be positive and negative deviations from the moving average by k standard deviations.

[0041] In a preferred embodiment, a sliding time period can be used to calculate the average power of the frequency band. and mean standard deviation .

[0042] In a preferred embodiment, the upper and lower limits of the Bollinger Bands can be calculated as follows:

[0043] Where k = {1, ..., 5}.

[0044] Advantageously, Bollinger Band calculation provides time-dependent settings for the first control parameter Pmax and the second control parameter Pmin, thereby enabling self-tuning of the control logic.

[0045] In some embodiments, the initialization and adjustment control logic can be an unsupervised learning model that establishes clusters of neural activity signal recordings based on at least one signal feature of the recordings. The unsupervised learning model can be implemented as a K-means clustering method. The K-means clustering method assigns signal recordings to K clusters based on the distance between the signal features and the centroid of each cluster, where K is a hyperparameter corresponding to the total number of clusters.

[0046] In some embodiments, at least one signal feature of the neural activity signal recording may be the spectral power calculated for each recording of the neural activity signal in a frequency band, and the number of clusters may be two, with control parameters Pmax and Pmin corresponding to the centroids of the respective clusters. In some variations, the unsupervised learning model may 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 may be predefined (e.g., two clusters) and set during and / or after acquiring the neural activity signal recordings.

[0047] In some embodiments, the initialization and adjustment logic can be a time-based fuzzy controller that estimates a new set of control parameters for the control logic based on at least one signal feature of the recorded neural activity signal and the relevant time slot of the day the neural activity signal was recorded. The time-based fuzzy controller can group and process at least one signal feature of the recorded neural activity signal based on the time slot of the corresponding neural activity signal.

[0048] In some embodiments, at least one signal feature of the neural activity signal recording may be the spectral power calculated for each neural activity signal recording in a frequency band, and the control parameters Pmax and Pmin may be equal to the average of the spectral power values ​​in a first set of spectral power values ​​and a second set of spectral power values, wherein each set of spectral power values ​​includes spectral power values ​​associated with neural activity signals recorded during a corresponding time slot in a day.

[0049] In some embodiments, the device for adaptive treatment of neurological disorders includes an implantable portion and an external portion, wherein a processing module configured to process neural activity signals recorded by an acquisition module based on initialization logic is included in the external portion. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of an exemplary device for adaptive treatment of nervous system diseases according to a preferred embodiment of the present invention; Figure 2 This is a block diagram of closed-loop control logic implemented by a method for controlling an apparatus for adaptive treatment of nervous system diseases according to a first embodiment of the present invention; Figure 3 This is a flowchart of a method for controlling an apparatus for adaptive treatment of nervous system diseases according to a first embodiment of the present invention; Figure 4 It is based on Figure 4 A flowchart of the initialization routine for a method of controlling a device for adaptive treatment of nervous system diseases; Figure 5 It is a spectrogram of neural signals and background activity; and Figure 6 It is a graph showing a first exemplary distribution of signal activity characteristics occurring at a value or within a predefined range; Figure 7 This is a graph illustrating a second exemplary distribution of signal activity characteristics occurring at a single value or within a predefined range; Figure 8a and Figure 8b It is a graph showing the distribution of stimulus values ​​at a certain value or within a predefined range, the distribution depending on the stimulus values ​​from, for example... Figure 7The selected initial control parameters are derived from the distribution of the signal activity characteristics shown. Figure 9 This is a block diagram of closed-loop control logic implemented by a method for controlling an apparatus for adaptive treatment of nervous system diseases according to a second embodiment of the present invention; Figures 10A to 10C It is based on Figure 9 A block diagram illustrating an exemplary embodiment of the initialization and adjustment logic implemented in the apparatus for adaptive treatment of nervous system diseases; Figure 11 These are schematic diagrams of exemplary devices for adaptive therapy of neurological disorders and related patient-personal controller devices; and Figure 12 This is a schematic block diagram of an exemplary system for adaptive treatment of nervous system diseases, the system including means for adaptive treatment of nervous system diseases according to the present invention. Detailed Implementation

[0051] In the accompanying drawings and the following description, the same reference numerals or symbols are used to indicate construction elements having the same function. Furthermore, for clarity, some reference numerals may not be repeated in all figures. While examples and variations of the invention are depicted and described herein, it should be understood that the invention is not intended to be limited to the specific examples and variations described below; rather, the invention is intended to cover all modifications, alternatives, and equivalent implementations falling within the scope of protection of the invention as defined in the claims.

[0052] Expressions such as “give an example,” “etc.”, and “or” indicate unrestricted, non-exclusive alternatives unless otherwise explicitly indicated. Expressions such as “comprise” and “include” have the meaning of “including or including but not limited to” unless otherwise explicitly indicated. Furthermore, as used throughout this document, “module” can refer to a component of circuitry and / or electrical parts arranged and connected to perform one or more functions as described herein, and / or can refer to a dedicated computer programmed to perform the functions described herein.

[0053] refer to Figure 1 This illustrates a variant of a device for adaptive therapy of neurological disorders, generally indicated by 10 (also referred to below as the "adaptive therapy device").

[0054] Specifically, Figure 1 The device shown is suitable for adaptive deep brain stimulation, which is configured to detect biopotentials (e.g., local field potentials or LFPs) from the stimulating electrode or from adjacent electrodes, for correlating such signals with stimulation effects and / or for adapting stimulation parameters to facilitate patient treatment.

[0055] The adaptive therapy device 10 includes at least one probe or electrocautery 11 configured to be implanted in a patient's brain to administer electrical stimulation. The probe or electrocautery 11 may include at least three metal contacts or leads, also referred to as electrodes 12, that can be contacted via external connectors. However, in other variations, the electrodes may not be located on the same electrocautery (e.g., a device for adaptive DBS may include two or more electrocauteries, and the electrodes may be located on two different electrocauteries).

[0056] The adaptive therapy device 10 may include one or more implantable probes, each probe including one or more electrodes. The device 10 may also include a connector or probe extension for each of the implantable probes. A probe (e.g., probe 11) may have a distal portion and a proximal portion. One or more electrodes (for delivering electrical stimulation and / or acquiring neural activity data) are located on the distal portion, and one or more connector contacts are located on the proximal portion, with one or more wires within the probe electrically connecting the electrodes to the connector contacts. Probe 11 may include 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 having a receiving housing surrounding one or more conductive contacts; a proximal portion having stimulation device (e.g., device 10) connector contacts, wherein each of the stimulation device connector contacts corresponds to a conductive contact in the receiving housing via one or more wires; and an elongated body located between the proximal and distal portions. The probe extension may include 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 stimulation device 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, and the probe extension is connected to the probe. The distal portion of the probe may be implanted into a target brain region, while the proximal portion of the probe may extend outside the brain tissue and connect to the distal portion of the probe extension. The receiving housing of the probe extension can be configured to hold the proximal portion of the probe such that the connector contacts of the probe are electrically connected to the conductive contacts of the probe extension, and that an electrode located at the distal portion of the probe is electrically coupled to a stimulation device connector contact located at the proximal portion of the probe extension. The stimulation device connector contacts can be configured to couple to a port or connector (e.g., a head interface) of the processing and stimulation unit 14. In some variations, the receiving housing may include an attachment mechanism to engage the proximal portion of the probe or to retain the proximal portion of the probe within the receiving housing. Optionally, the probe extension may include a connector sleeve or sheath comprising an electrically insulating material disposed over at least a portion of the receiving housing to facilitate electrical isolation of 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 varying diameter along its length. For example, the diameter of a segment of the elongated body may be larger (e.g., thicker) at the location where the segment is intended to be located at the interface between brain tissue and the skull or skin.This can help reduce excessive winding, twisting, and / or bending of the wires within the slender body of the probe extension, thereby reducing mechanical wear on the wires and / or helping to extend the service life of the probe extension.

[0057] Although Figure 1 The adaptive therapy device 10 depicted includes probes 11 having four metal contacts or electrodes 12, but other variations of the probes 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 described, the adaptive therapy device 10 may include any number of probes (e.g., two or more), wherein each probe may have any number of electrodes. For example, the adaptive therapy device 10 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 adaptive therapy device 10 may include two probes, wherein each probe may have four electrodes (eight channels in total) or may have eight electrodes (sixteen channels in total).

[0058] In one variant, probe 11 may include multiple electrodes, wherein a first electrode is a stimulating electrode for delivering electrical stimulation, and a second electrode is a measuring electrode for acquiring neural activity signals. For example, multiple first electrodes (which may be adjacent to or not adjacent to each other) may be used for stimulation, and multiple second electrodes (which may be adjacent to or not adjacent to each other, or may be arranged alternately with multiple first electrodes) may be used for acquiring neural activity signals. Alternatively or additionally, the same electrodes may be used simultaneously or sequentially for acquiring neural activity signals and for electrical stimulation. The DBS probe may include one or more cylindrical or disc-shaped electrodes having a height from about 0.5 mm to about 3 mm (e.g., about 1.5 mm) and a diameter from about 0.5 mm to about 2 mm (e.g., about 1.27 mm). In some variants, the DBS probe may include two or more cylindrical electrodes (e.g., 2, 4, 6, 10, 12, 15, 16, 20, etc., or more electrodes). Alternatively or additionally, the DBS probe may include planar electrodes and / or sharp electrodes having a geometry at least partially selected based on a target neural structure or brain region. The spacing between the two electrodes can range from about 0.25 mm to about 2 mm, for example, about 0.5 mm, and optionally, an insulator can 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. The electrodes can be made of any metal or any metal alloy, such as a platinum-iridium alloy.

[0059] existFigure 1 In the illustrated embodiment, electrode 12 is connected to processing and stimulation unit 14, which includes three functional modules connected together in a feedback and interoperability configuration: stimulation module 16, data acquisition module 20, and processing module 18. Processing and stimulation unit 14 may be entirely included in the implantable portion 10a of the adaptive therapy device 10 or distributed between the implantable portion 10a and the external portion 10b. In this second case, the recording of line and / or processed neural activity signals is wirelessly transmitted from the implantable portion 10a to the external portion 10b, and vice versa. The external portion 10b may be implemented as a patient-personal controller device. The implantable portion 10a and the external portion 10b of the adaptive therapy device 10 will be described in detail below and Figure 7 The image is depicted schematically.

[0060] In one variant, the processing and stimulation unit 14 may include sixteen channels that can be connected to two probes, each with eight electrodes, or to four probes, each with four electrodes, or to eight probes, each with two electrodes, and so on. The number of electrodes may be fewer than the number of channels; for example, while the processing and stimulation unit 14 may be configured to accommodate sixteen channels (e.g., for sixteen stimulation and / or LFP acquisition electrodes), specific instances of the adaptive therapy device 10 or the DBS system may include eight electrodes (e.g., two probes, each with four electrodes) or four electrodes (e.g., a single probe with four electrodes).

[0061] The stimulation module 16 is used to generate stimulation signals and send them to the electrode 12. The stimulation module 16 may include a pulse or function generator, which includes a voltage source and / or a 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 for transmitting the electrical pulses to a probe that delivers the electrical pulses to a brain region.

[0062] In some variations, the stimulation module may include a waveform generator (e.g., a pulse or function generator), a current controller, and a multiplexer, one or more of which may be configured to receive a command signal from the processing module 18. The command signal may include electrical stimulation parameter data, including but not limited to stimulation amplitude, pulse width, pulse frequency, duty cycle, and / or a specific probe and / or electrode to which the electrical stimulation is delivered with specified parameters. 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 a current pulse or voltage pulse having a pulse width and / or pulse frequency specified by the command signal. The multiplexer may be configured to electrically connect the probe and / or electrode specified by the command signal to the current controller and / or the waveform generator. In some variations, the multiplexer may include a multiplexer array that can be configured, based on a command signal from the main processor, such that electrical pulses from the waveform generator can be directed to selected probes and / or electrodes. The connection between the waveform generator and the electrode may be arranged via the multiplexer in a monopolar stimulation configuration and / or a bipolar stimulation configuration. In a monopolar configuration, one or more electrodes may be connected to one or more active (e.g., positive) terminals of a waveform generator (with a return pad placed elsewhere on the patient's body). 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 a 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.

[0063] In some variations, stimulation module 16 may be configured to generate a stimulation signal that can be characterized by a set of parameters and to transmit the stimulation signal to one or more electrodes in electrode 12. For example, stimulation module 16 may include a pulse generator having a current source (and / or voltage source) that generates an electrical signal having parameters specified by a user and / or processing module. In some variations, the pulse generator may generate an output pulse having specified amplitude, frequency, and / or pulse width or duration values. Optionally, the pulse generator may generate a pulse sequence having two or more pulses that are repeated at a duty cycle specified by a user and / or processing module 18, and 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).

[0064] Data acquisition module 20 is responsible for acquiring signals representing brain activity from the patient's brain, such as local field potentials (LFPs) that may represent brain activity in the brain region where probe 11 is implanted. Acquisition module 20 is electrically connected to probe 11, which in some variations may be the same probe used for electrically stimulating the brain region. Acquisition module 20 and / or probe 11 may be configured to acquire neural activity signals, such as local field potentials (LFPs), generated by activity in brain regions adjacent to probe 11. Acquisition module 20 may include an acquisition processor and a memory that store and analyze the acquired neural activity signals.

[0065] The processing module 18 implements adaptive control of the stimulation module 16 based on the 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. Furthermore, the processing module may have circuitry configured to coordinate signal transmission between the acquisition module 20 and the stimulation module 16, and / or circuitry configured to perform additional calculations on the acquired neural activity signals.

[0066] The processing module 18 may be part of the acquisition module or the stimulation module, or it may be a separate module. In some variations, the processing module includes circuitry configured to regulate / coordinate the operation of the stimulation module based on signals from the acquisition module (e.g., based on LFP signals indicating neural activity). The processing module may have a module (main) processor and memory configured to analyze and store the acquired neural activity signals and / or signals from the acquisition module. In some variations, the processing module may include circuitry configured to, for example, regulate the power supplied to the stimulation module in coordination with electrical stimulation parameters determined by the acquisition module and / or the acquired neural activity signals. 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 signals to identify changes or alterations in patterns or characteristics of the neural activity signals. The processing module may provide command signals to the pulse generator of the stimulation module to change the electrical stimulation parameters according to changes in the neural activity signals 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 the battery and / or measure its remaining charge. 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 a stimulation device (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 implanted stimulation device and the external device. In some variations, the acquisition module may include a processor configured to calculate the spectral power value of the acquired neural activity signal, and the calculated power value may be transmitted to the processing module. The processing module processor may be configured to derive stimulation parameters based on a general command signal to a pulse generator and the power value, to adapt or adjust the parameters of the electrical stimulation. Optionally, the processing module may include additional submodules having circuitry configured for power management, electrode impedance checking, and / or calibration and / or diagnostic analysis (e.g., troubleshooting) of the stimulation module.

[0067] Back Figure 1 The acquisition module 20 in the middle has the main function of measuring the electric field change of local biopotential, directly sensing the potential difference associated with the common electrode 17 and amplifying the potential difference, thereby achieving a voltage level useful for analog-to-digital conversion required for signal processing.

[0068] Therefore, the acquisition module 20 may include: input ports, each connected to a different electrode 12 on the probe 11; and circuitry configured to measure changes in the electric field of the local biopotential or local field potential (LFP) based on signals from the input ports. The circuitry of the acquisition module may include one or more processing units or processors (e.g., CPU, and / or one or more field-programmable gate arrays, and / or one or more application-specific integrated circuits) that can 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.

[0069] Figure 2 This is a functional block diagram illustrating the operation of the acquisition module 20 and processing module 18 of the processing and stimulation unit 14 according to a first embodiment of the present invention. As previously described, at block 21, the acquisition module 20 can calculate a set of signal characteristics of the acquired neural activity signal, such as spectral power values. The spectral power values ​​can be transmitted to the processing module 18, and the processor of the processing module can apply control logic (as shown in block 22) to the received spectral power values ​​to derive stimulation parameters to adapt or adjust the parameters of electrical stimulation.

[0070] The control logic of box 22 may depend on at least one signal feature of the neural activity signal recording. F i ( i =1、……、 N The function can be based on at least one control parameter. C j ( j =1、……、 M The control logic can consist of multiple function segments, each of which is associated with at least one signal feature. F i The corresponding range is relevant. At least one of the multiple function segments can be at least one signal feature dependent on the recording of neural activity signals. F i The function. At least one of the multiple function segments can be at least one signal feature of a neural activity signal recording. F i A linear function. At least one of the multiple function segments can be a constant value.

[0071] For example, in one implementation, the control logic may include a first function segment in which the stimulus parameter A and signal characteristics are... F Proportional. The first function segment can be proportional to the first range of signal characteristics ( C 2< F <C 1) Correlation. The control logic may include a second function segment that defines an upper limit Amax of the stimulus parameter A. The second function segment may be correlated with a second range of signal characteristics ( F > C 1) Related. The control logic may also include a third function segment that defines the lower limit Amin of the stimulus parameter A. The third function segment may be related to a third range of signal characteristics ( F < C 2) Relevance. The upper and lower limits of the stimulus parameter A can be used to define the stimulus parameter window (Amax, Amin).

[0072] Therefore, the control logic can be as follows:

[0073] In this case, the control logic is determined by the first control parameter. C 1 and second control parameters C 2. Characterization.

[0074] For example, in one implementation, at least one signal feature of the recorded neural activity signal can be a spectral feature within a frequency band, preferably the power of the neural activity signal within that band. The frequency band can be a low-frequency band, an alpha band, or a beta and gamma band. The frequency band can be preset (hard-coded) or input as a non-tunable parameter.

[0075] Therefore, the control logic may include a first function segment in which the stimulus parameter A is proportional to the power of the signal in the frequency band. The first function segment may be related to a first power range (…). Pmin < P < Pmax The control logic may include a second function segment that defines an upper limit Amax for the stimulus parameter A. This second function segment may be related to a second power range (…). P > Pmax The control logic may also include a third function segment that defines the lower limit Amin of the stimulus parameter A. This third function segment is related to the third power range (…). P < Pmin (Related to)

[0076] In this implementation, the control logic can be as follows:

[0077] In this case, the 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.

[0078] In some implementations, at least one signal feature may be one or any combination of signal amplitude, signal phase, entropy, inter-signal or intra-signal coherence, inter-phase or intra-phase amplitude coupling, fractal spectrum, fractal dimension, phase lock value, modulation index, kurtosis, fluctuation index, etc.

[0079] The processing module can also be configured to process neural activity signal recordings received from the acquisition module based on initialization logic 300 (shown at block 23), and to initialize at least one control parameter of the control logic in block 22 based on the neural activity signal recordings processed according to the initialization logic. C 1. C 2.

[0080] Figure 3 This is an exemplary method 200 according to the invention for controlling an apparatus for adaptive treatment of neurological disorders, which implements a routine 300 (initialization logic) for initializing control logic based on collected neural signals. Method 200 includes: at 210, the step of acquiring and / or storing neural activity signal recordings; and at 220, further processing the acquired neural activity signal recordings and extracting neural signal features. If the process has just begun and the control logic still needs initialization (i.e., its control parameters still need to be set for the first time), the steps of acquiring and / or storing neural activity signal recordings and further processing the acquired neural activity signal recordings are preferably performed without stimulation and / or medication, or based on a time series of neural signal activity recordings lasting for hours, days, weeks, or months using preset stimulation values, and at 230 and 240, the method uses the neural signal features extracted from the acquired neural activity signal recordings to calculate initial control parameters (as described in detail below). Steps 230 and 240 implement the initialization logic 300. Adaptive stimulation finally begins at 250 and is recursively adapted at 260.

[0081] Figure 4 This is an exemplary routine 300 for initializing control logic, specifically for situations where the control logic is a function of the spectral power of the neural activity signal in the frequency band, and the control parameters of the control logic are first control parameters corresponding to the maximum spectral power Pmax. C 1 and the second control parameter corresponding to the minimum spectral power Pmin C 2. Routine 300 is used to process neural activity recordings acquired under no-stimulation conditions or with preset stimulus values, based on time series of neural activity recordings lasting for hours, days, weeks, or months. The neural activity recordings are processed at 301 to extract the signal spectrum 601 (in...). Figure 5(As shown in the figure) and determine the peak frequency 603 of the recorded neural activity signal. Based on the position of the peak within the frequency range, a patient-specific frequency band is selected and set to surround the peak, preferably centered on the peak.

[0082] At 302, initialization routine 300 determines the spectral power 605 of the recorded neural activity signal in a selected patient-specific frequency band. Then, at 303, the routine counts the number of times the extracted signal power appears substantially at a given value or within one of a plurality of predefined ranges during an observation period lasting several hours, days, or months, and based on the counting step 303, at 304 determines the distribution of the occurrence of the extracted signal power.

[0083] Finally, based on the probability distribution of the extracted signal power (hereinafter also referred to as the "distribution"), a first control parameter Pmax and a second control parameter Pmin can be set at 305. For example, the first control parameter Pmax is set to a value higher than the most frequent power value (i.e., the extracted signal power value that occurs most often), and the second control parameter Pmin is set to a value lower than the most frequent power value.

[0084] Overall, in order to set the initial control parameters C 1. C 2. The initialization logic 300 implemented by block 23 includes step 303: based on a time series of neural signal activity recordings lasting several hours, days, or months, counting the number of times the extracted neural signal features substantially appear at a given value or within one of several predefined ranges. Based on the result of counting step 303, the occurrence distribution of the values ​​of the extracted neural signal features is obtained (step 304), and at 305, the first control parameter is initialized by processing the occurrence distribution of the values ​​of the extracted neural signal features. C 1 and second control parameters C 2.

[0085] In a preferred embodiment, the first control parameter C 1 is selected as the value that appears most frequently than at least one neural signal feature (hereinafter also referred to as the "most frequent feature value"), and the second control parameter C 2 was selected as the least frequent feature.

[0086] In an alternative embodiment, the initialization logic is configured to initialize a second control parameter from at least one control parameter of the control logic. C The initial value of 2 is set to the k1th percentile of the distribution, where k1 is equal to or less than 50, and the first control parameter in at least one control parameter of the control logic is... CThe initial value of 1 is set to the k2th percentile, where k2 is equal to or greater than 50.

[0087] The characteristic values ​​of neural activity recordings have, for example, Figure 6 In another preferred embodiment of the normal distribution (e.g., Gaussian or uniform distribution) shown, the most frequent eigenvalue is the distribution mean. And the first control parameter C 1 and second control parameters C 2 is set to:

[0088] Where k = {1, ..., 5}, and σ is the standard deviation of the normal distribution.

[0089] exist Figure 7 In the variant of the invention shown, the values ​​of the features of the neural activity recording have a bimodal distribution, that is, a distribution showing two peaks, namely a first peak at a higher feature value indicating a higher pattern and a second peak at a lower feature value indicating a lower pattern.

[0090] In the first case, the first control parameter C 1a It can be set to a feature value equal to the peak occurrence within the higher modes of the distribution, and the second control parameter C 2 can be set to the characteristic value of the peak occurrence within the lower pattern of the distribution.

[0091] In the second case, the second control parameter C 2 can be set to an eigenvalue equal to the peak occurrence within the lower modes of the distribution, and the first control parameter C 1b The expected stimulus value can be arbitrarily set based on its occurrence, for example, equal to or higher than the highest signal characteristic value. In fact, the expected stimulus value varies between an upper limit Amax and a lower limit Amin, and exhibits an occurrence distribution following a curve. The development of this curve correlates with the characteristic values ​​of the neural activity recording used to set the first control parameter. C The characteristic value of 1 and the parameter used to set the second control parameter C The distribution of the eigenvalues ​​of 2 is proportional.

[0092] Therefore, the first control parameter is selected based on the occurrence distribution of feature values ​​from neural activity recordings. C 1 and second control parameters C 2. It can set the distribution of stimulus values.

[0093] Advantageously, the distribution of the extracted signal power determined at 304 can be visually displayed on the clinician programmer device 131 (in Figure 12(As shown in the image), this allows the selection of the first control parameter based on the occurrence of the desired stimulus value. C 1 and second control parameters C 2.

[0094] Figure 8a and Figure 8b The above text is presented in combination with... Figure 7 The embodiments described in the first and second cases relate to the first control parameter. C 1 and second control parameters C 2. The expected stimulus value distribution obtained by initialization.

[0095] Figure 9 This is a functional block diagram illustrating the operation of the acquisition module 20 and processing module 18 of the processing and stimulation unit 14 according to a second embodiment of the present invention. Figure 2 Unlike the first embodiment shown, the processing module can be configured to process the neural activity signal recordings received from the acquisition module based on initialization and adjustment logic 23'. Therefore, in addition to at least one control parameter of the control logic for block 22... C 1. C In addition to initialization, the processing module can also be configured to tune at least one control parameter based on neural activity signal recordings processed according to initialization and adjustment logic 23'. C 1. C 2.

[0096] Figures 10A to 10C Processing module 18 can be used to tune control parameters. C 1. C Example of initialization and adjustment logic 23', where these control parameters characterize the control logic.

[0097] exist Figure 10A In the example, the initialization and adjustment logic can be implemented as Bollinger Bands calculation, where the first control parameter... C 1 is set to equal the upper limit of the Bollinger Bands, and the second control parameter C 2 is set to be equal to the lower Bollinger Band. Both the upper and lower Bollinger Bands can be calculated based on power time series collected over minutes, hours, days, weeks, or months.

[0098] Bollinger Bands can be calculated based on a simple moving average over any time period (e.g., 30 minutes, one hour, one day, one week, etc.). Alternatively, Bollinger Bands can be calculated based on an exponential moving average. The upper and lower limits of the Bollinger Bands can be k standard deviations, representing positive and negative deviations from the moving average.

[0099] Average power of the frequency band and mean standard deviation The initialization and adjustment logic can be used to calculate the upper and lower limits of the Bollinger Bands over a sliding time period (e.g., a 24-hour sliding time period). Therefore, at each time step after 24 hours (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 previous 24 hours.

[0100] The upper and lower limits of the Bollinger Bands can be calculated as follows:

[0101] Where k = {1, ..., 5}.

[0102] The time period used for moving average and standard deviation calculations, and the width of the Bollinger Bands given by factor k, are hyperparameters for Bollinger Band calculations.

[0103] In some variations, Bollinger Band calculation can be applied to control parameters of control logic, defining thresholds and allowing for dynamic tuning. The control logic can control the on / off state of stimuli based on a threshold value of at least one signal characteristic of a neural activity signal recording. The threshold can be calculated as an upper or lower limit of the Bollinger Bands.

[0104] exist Figure 10B In the example, the initialization and adjustment 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 of the recordings. The unsupervised learning model can be implemented as a K-means clustering method. The K-means clustering method assigns signal recordings to K clusters based on the distance between the signal features and the centroid of each cluster, where K is a hyperparameter corresponding to the total number of clusters.

[0105] For example, at least one signal feature of the recorded neural activity signal can be the spectral power calculated for each recording of the neural activity signal in a frequency band, and the number of clusters can be two, with control parameters Pmax and Pmin corresponding to the centroids of the respective clusters. Therefore, the calculated spectral power closer to the cluster centroid will be classified into the corresponding cluster. The value of each centroid is updated after the calculated spectral power is assigned to the corresponding cluster. The control parameters Pmax and Pmin, determined during initialization routine 300, provide the initial values ​​for the cluster centroids and will iteratively change as calculated spectral power values ​​are added to the corresponding clusters. This allows the control parameters Pmax and Pmin to be adjusted over time based on the development of the recorded neural activity signal.

[0106] In some variants, unsupervised learning models can be implemented as exclusive (e.g., k-means), overlapping (e.g., fuzzy k-means), hierarchical, or probabilistic (e.g., Gaussian mixture models) clustering models. The number of clusters can be set or predefined (e.g., two clusters) during and / or after acquiring neural activity signal recordings.

[0107] exist Figure 10C In the example, the initialization and adjustment logic 23' can be implemented as a time-based fuzzy controller that estimates a new set of control parameters for the control logic based on at least one signal feature of the recorded neural activity signal and the associated time slot of its recording date. The time-based fuzzy controller can group and process at least one signal feature of the recorded neural activity signal based on the time slot of the corresponding neural activity signal recorded in a day.

[0108] For example, in one implementation, signal features of neural activity signals recorded between 10:00 AM and 12:00 PM and between 2:00 PM and 4:00 PM can be grouped into a first group, and signal features of neural activity signals recorded between 8:00 AM and 10:00 AM and between 12:00 PM and 2:00 PM can be grouped into a second group. For example, at least one signal feature of the neural activity signal recordings can be the spectral power calculated for each recording of the neural activity signal within a frequency band. Control parameters Pmax and Pmin can be equal to the average of the spectral power values ​​of the first and second groups, respectively. In real-time applications, control parameters Pmax and Pmin can be updated daily, weekly, or monthly. The time slots within a day used to group the signal features of the neural activity signals can be preset.

[0109] Figure 11This is a schematic diagram of an exemplary method for establishing a wireless connection between an implantable portion 10a (also referred to herein as an "implantable pulse generator (IPG) device") and an external portion 10b (also referred to herein as a "patient personal controller device") of an adaptive therapy device 10. The patient personal controller device 10b can wirelessly transmit power to charge the IPG device 10a. Alternatively or additionally, the IPG device 10a can transmit and receive neural activity signal recordings and / or stimulation parameters to and from the patient personal controller device 10b. For example, the IPG device 10a can wirelessly transmit neural activity signal recordings to the patient personal controller device 10b, and the personal controller device 10b can wirelessly transmit stimulation parameters or instructions to the IPG device 10a. In some instances, a wireless connection can be established between the patient personal controller device 10b and the implantable device 10a (e.g., a rechargeable unit of the patient personal controller device) when the patient personal controller device 10b and the implantable device 10a are within a predetermined distance range (e.g., 2 cm to 10 cm, 1 mm to 1 m, etc.) and orientation range. The orientation range may involve, for example, aligning the vertical orientation of the patient personal controller device with the vertical orientation of the implantable device within an error range of 5 rotation degrees, 10 rotation degrees, etc. 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.

[0110] Figure 12 An exemplary embodiment of a system 100 for adaptive therapy of neurological disorders is shown. Figure 12As shown, a system 100 for adaptive therapy of neurological disorders may include an IPG device 10a that acquires and stores neural activity signal recordings and applies electrical stimulation. The system 100 also includes a patient personal controller device 10b that establishes a wireless connection with the IPG device 10a to receive neural activity signal recordings and recharge the battery of the IPG device 10a. The patient personal controller device 10b transmits power to the IPG device 10a, and the IPG device 10a transmits neural activity signal recordings to the patient personal controller device 10b via the wireless connection. The patient personal controller device 10b may be operatively coupled (e.g., via Bluetooth, WiFi, etc.) to a user computing device 121 and transmit neural activity signal recordings and / or patient log data to the user computing device 121. The user computing device 121 analyzes the neural activity signal recordings and / or patient log data. The system 100 for adaptive therapy of neurological disorders also includes a clinician programmer device 131, which establishes a second wireless connection with the IPG device 10a based on the activation of a first wireless connection, receives neural activity signal recordings, and sets stimulation parameters based on the neural activity signal recordings. The clinician programmer device 131 can also be configured to implement initialization logic 23, 23' and transmit at least one neural activity signal recording processed according to the initialization logic 23, 23' to the processing module 18 of the adaptive therapy device 10.

[0111] User computing device 121 can also be configured to connect to network 150 via a network connection (e.g., WiFi connection, 5G network connection, etc.) and transmit neural activity signal recordings, patient log data and / or stimulation parameters to biobank server 160 via network 150.

[0112] In some instances, the user computing device 121 includes a graphical user interface (GUI) and displays a graph or statistical distribution of the neural activity signal recordings via the GUI. The statistical distribution of the neural activity recordings may include, for example, a moving average, a daily average, a weekly average, the variance of the neural activity recordings, local maxima, local minima, global maxima, global minima, etc. In some instances, to determine the drug on-time and drug off-time intervals, the user computing device 121 processes (e.g., extracts, displays, etc.) a set of spectral characteristics within a frequency band of the neural activity signal recordings recorded within a predetermined time period. The frequency band may include a low-frequency band, an alpha band, a beta band, a gamma band, etc. The user computing device 121 may generate stimulation parameters based on initialization and adjustment logic 23'.

[0113] In some embodiments, the clinician programmer device 131 is not operatively coupled to the IPG device 10a, and the patient personal controller device 10b may be operatively coupled (e.g., via Bluetooth, WiFi, etc.) to the clinician programmer device 131 and transmit neural activity signal recordings and / or patient log data to the clinician programmer device 131. In such embodiments, the clinician programmer device 131 analyzes the neural activity signal recordings and / or patient log data based on the neural activity signal recordings, and generates a set of stimulation parameters based on initialization and adjustment logic 23'. The clinician programmer device 131 may also be configured to connect to a network 150 via a network connection (e.g., WiFi, etc.) and transmit neural activity signal recordings, patient log data, and / or stimulation parameters to a biobank server 160 via the network 150.

[0114] In some variations, a conventional DBS (cDBS) treatment modality can be used instead of an adaptive DBS (aDBS) treatment modality. For example, monitoring / observation of one month's worth of neural activity data (e.g., local field potential activity stored as a numerical time series) can be stored in the patient controller 10b and / or IPG device 10a, and then transmitted to the user computing device 121 and / or clinician programmer device 131 for analysis. The user computing device 121 and / or clinician programmer device 131 can then generate and transmit a set of stimulation parameters and aDBS treatment modality to the patient controller 10b and / or IPG device 10a for use.

[0115] For illustrative purposes, the above description specifically refers to DBS applications. However, the invention can also be applied to different implementations, such as SCS for pain management. 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 in the spine. The spinal response to electrical stimulation is the sum of activations of a cluster of nerve fibers, i.e., evoked compound action potentials (ECAPs). The processing module can be configured to calculate a set of signal characteristics of the acquired neural activity signal (e.g., the spectral power of the ECAP). The spectral power value can be transmitted to the processing module, and the processor of the processing module can apply control logic to the received signal characteristics to derive stimulation parameters, thereby adapting or adjusting the parameters of the electrical stimulation. The processor of the processing module can also apply initialization and adjustment logic to adjust the control parameters of the control logic as described above. The foregoing considerations for implementing the control logic and the initialization and adjustment logic still apply.

[0116] The foregoing description uses specific terminology for illustrative purposes to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that specific details are not required to practice the invention. Therefore, the foregoing description of specific variations of the invention is presented for illustrative and descriptive purposes. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed; obviously, many modifications and variations are possible in light of the foregoing teachings. These variations have been chosen and described to explain the principles of the invention and its practical application, thereby enabling those skilled in the art to utilize the invention as well as various variations and modifications suited to the specific intended use. It is intended that the following claims and their equivalents define the scope of the invention.

Claims

1. A device (10) for the adaptive treatment of a neurological disorder, comprising: an implantable electrode (11) configured to sense a neural activity signal and to apply an electrical stimulation signal, and a processing and stimulation unit (14) connected to the implantable electrode (11), wherein the processing and stimulation unit comprises at least: a stimulation module (16) configured to generate a stimulation signal to be carried at the implantable electrode (11), the stimulation signal being characterized by at least one stimulation parameter (A); an acquisition module (20) configured to record neural activity signal recordings of the neural activity signal sensed by the implantable electrode; and a processing module (18) configured to process the neural activity signal recordings recorded by the acquisition module (20) based on a control logic (22) and to tune the at least one stimulation parameter (A) based on at least one neural activity signal recording processed according to the control logic (22), the control logic being a function of at least one signal feature (Fi) of the neural activity signal recordings, based on at least one control parameter (Cj) and composed of a plurality of function segments, wherein each function segment of the plurality of function segments is associated with a respective range of the at least one signal feature (Fi), wherein the processing module (18) is further configured to process the neural activity signal recordings recorded by the acquisition module (20) based on an initialization logic (23, 23') and / or to receive at least one neural activity signal recording processed according to an initialization logic (23, 23') and to initialize at least one control parameter (Cj) of the control logic (22) based on at least one neural activity signal recording processed according to the initialization logic (23, 23'); and wherein the initialization logic (23, 23') is configured to extract at least one signal feature (Fi) from the neural activity signal recordings and to determine a distribution of the at least one extracted signal feature (Fi) based on a number of times a given value of a plurality of given values is substantially reached or a number of times a given range of a plurality of given ranges is included by the extracted signal feature (Fi).

2. The device (10) according to claim 1, wherein, The initialization logic (23, 23') is configured to select an initial value of a first control parameter (C1) of the at least one control parameter (Cj) of the control logic (22) to be higher than a value of the most frequently occurring extracted signal feature (Fi), and to select an initial value of a second control parameter (C2) of the at least one control parameter (Cj) of the control logic (22) to be lower than a value of the most frequently occurring extracted signal feature (Fi); and / or C 1) and to select an initial value of a second control parameter (C2) of the at least one control parameter (Cj) of the control logic (22) to be lower than a value of the most frequently occurring extracted signal feature (Fi); and / or C 2) wherein said initialization logic (23, 23') is configured to set an initial value of a first control parameter (C1) among the at least one control parameter (Cj) of the control logic (22) equal to a k1th percentile of said distribution, wherein k1 is equal to or greater than 50, and to set an initial value of a second control parameter (C2) among the at least one control parameter (Cj) of the control logic (22) equal to a k2th percentile of said distribution, wherein k2 is equal to or less than 50. C 1) C 2) 3. The apparatus (10) according to claim 1 or 2, wherein The distribution of the at least one extracted signal feature (Fi) is a normal distribution, and the initialization logic (23, 23') is configured to select initial values for a first control parameter (C1) and a second control parameter (C2) of at least one control parameter (Cj) of the control logic (22) as: C 1) the mean value of the distribution of the at least one extracted signal feature (Fi), and C 2) the standard deviation of the distribution of the at least one extracted signal feature (Fi). where k = {1,..., 5}, is the distribution mean and σ is the standard deviation of the distribution.

4. The apparatus (10) according to claim 1, wherein the distribution of the at least one extracted signal feature (Fi) has a first peak at higher signal feature (Fi) values and a second peak at lower signal feature (Fi) values; and wherein the initialization logic (23, 23') is configured to set an initial value of a first control parameter (C1) of the at least one control parameter (Cj) of the control logic (22) to be equal to the higher signal feature (Fi) value at the occurrence of the first peak and to set an initial value of a second control parameter (C2) of the at least one control parameter (Cj) of the control logic (22) to be equal to the lower signal feature (Fi) value at the occurrence of the second peak; or C 1a C 2​ wherein the initialization logic (23, 23') is configured to set an initial value of a second control parameter (C2) of the at least one control parameter (Cj) of the control logic (22) equal to the lower signal characteristic (Fi) value at the second peak occurrence, and to set an initial value of a first control parameter (C1) of the at least one control parameter (Cj) of the control logic (22) equal to or higher than the highest signal characteristic (Fi) value. C 1b ​ 5. The apparatus (10) according to any one of the preceding claims, wherein, the distribution of the at least one extracted signal feature (Fi) is based on a time series of neural signal activity recordings lasting hours, days, weeks or months.

6. The apparatus (10) according to any one of the preceding claims, wherein at least one function segment of the plurality of function segments of the control logic (22) is a function of the at least one signal feature (Fi) of the neural activity signal recordings, preferably a linear function of the at least one signal feature (Fi) of the neural activity signal recordings.

7. The apparatus (10) according to any one of the preceding claims, wherein The initialization logic is an initialization and adjustment logic (23') and the processing module (18) is further configured to process the neural activity signal recordings recorded by the acquisition module (20) based on the initialization and adjustment logic (23') and to tune at least one control parameter (Cj) of the control logic (22) based on at least one neural activity signal recording processed according to the initialization and adjustment logic (23'), and wherein the initialization and adjustment logic (23') is a Bollinger Bands calculator, wherein a first control parameter (C1) of the control logic (22) is set equal to a Bollinger Bands upper limit and a second control parameter (C2) of the control logic (22) is set equal to a Bollinger Bands lower limit; or wherein the initialization and adjustment logic (23') is an unsupervised learning model which establishes, for each control parameter (Cj) of the control logic (22), one cluster of the neural activity signal recordings based on at least one signal feature (Fi) of the neural activity signal recordings, wherein each control parameter (Cj) corresponds to a centroid of the respective cluster; or wherein the initialization and adjustment logic (23') is a time-based fuzzy controller which estimates at least one control parameter (Cj) of the control logic (22) based on at least one signal feature of the neural activity signal recordings and a relevant time slot of the day when the neural activity signal recordings were acquired.

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

9. The apparatus (10) according to claim 8, wherein The control logic (22) comprises a first function section in which at least one stimulation parameter (A) is proportional to the spectral power (P) of the neural activity signal recording in a frequency band, the first function section being associated with a first power range (P1) comprising between a minimum spectral power (Pmin) and a maximum spectral power (Pmax). Pmin P Pmax )​​ 10. The apparatus (10) of claim 9, wherein, The control logic (22) is wherein Amin and Amax are a lower limit and an upper limit of the stimulation parameter (A), respectively.

11. The device (10) according to any one of the preceding claims, comprising an implantable part (10a) and an external part (10b), wherein, The processing module (18) configured to process the neural activity signal recordings recorded by the acquisition module (20) based on the initialization logic (23, 23') is comprised in the external portion (10b).

12. A system (100) for the adaptive treatment of neurological disorders, the system comprising: a device (10) for the adaptive treatment of neurological disorders according to any one of the preceding claims, and a clinician programmer device (131) configured to be connected to the device (10) for the adaptive treatment of neurological disorders to receive neural activity signal recordings, to process the received neural activity signal recordings based on the initialization logic (23, 23') and to transmit at least one neural activity signal recording processed according to the initialization logic (23, 23') to the processing module (18) of the device (10) for the adaptive treatment of neurological disorders.

13. The system (100) for the adaptive treatment of neurological systems diseases according to claim 12, wherein, The clinician programmer device (131) is configured to visually display the at least one signal feature (Fi) extracted from the neuroactivity signal recordings.

14. A method (100) for controlling a device (10) for the adaptive treatment of neurological disorders, comprising the steps of: - processing neuroactivity signal recordings recorded by an acquisition module (20) of the device (10) for the adaptive treatment of neurological disorders based on a control logic (22), wherein the control logic (22) is a function of at least one signal feature (Fi) of the neuroactivity signal recordings, is based on at least one control parameter (Cj) and is composed of a plurality of function segments, each function segment of the plurality of function segments being related to a respective range of the at least one signal feature (Fi), - tuning at least one stimulation parameter (A) of a stimulation signal based on at least one neuroactivity signal recording processed according to the control logic (22); - processing at least one neural activity signal recording based on initialization logic (23, 23') to extract at least one signal feature (S) from the neural activity signal recording, F i ) and determining a distribution of the at least one extracted signal feature (S) based on the extracted signal feature (S) substantially reaching one of a plurality of given values or being included in one of a plurality of given ranges a number of times, F i F i ​​ - initializing at least one control parameter (Cj) of the control logic (22) based on at least one neuroactivity signal recording processed according to the initialization logic (23, 23’).

15. The method (100) according to claim 14, further comprising the steps of: - based on the initialization logic (23, 23'), selecting an initial value of a first control parameter (C1) of the at least one control parameter (Cj) of the control logic (22) to be higher than a value of the most frequently occurring extracted signal feature (Fi), and selecting an initial value of a second control parameter (C2) of the at least one control parameter (Cj) of the control logic (22) to be lower than a value of the most frequently occurring extracted signal feature (Fi); and / or C 1) and / or C 2). On the basis of said initialization logic (23, 23'), an initial value of a first control parameter (C1) among the at least one control parameter (Cj) of the control logic (22) is set equal to a k1th percentile of said distribution, with k1 equal to or greater than 50, and an initial value of a second control parameter (C2) among the at least one control parameter (Cj) of the control logic (22) is set equal to a k2th percentile of said distribution, with k2 equal to or less than 50. C 1) C 2) 16. The method (100) of claim 14 or 15, wherein The distribution of the at least one extracted signal feature (Fi) is a normal distribution, and based on the initialization logic (23, 23'), the method further comprises the step of selecting initial values for a first control parameter (C1) and a second control parameter (C2) of the at least one control parameter (Cj) of the control logic (22) as: C 1) the mean value of the distribution of the at least one extracted signal feature (Fi), and C 2) the standard deviation of the distribution of the at least one extracted signal feature (Fi). where k = {1, 5}, is the mean of the distribution and σ is the standard deviation of the distribution.

17. The method (100) of claim 14, wherein the distribution of the at least one extracted signal feature (Fi) has a first peak at higher signal feature (Fi) values and a second peak at lower signal feature (Fi) values; and wherein based on the initialization logic (23, 23’), the method (100) comprises the steps of: - setting an initial value of a first control parameter (C j ) among the at least one control parameter (C C 1a ) of the control logic (22) equal to the higher signal feature (F i ) value, and setting an initial value of a second control parameter (C j ) among the at least one control parameter (C C 2) of the control logic (22) equal to the lower signal feature (F i ) value; or - setting an initial value of a second control parameter (C2) of the at least one control parameter (Cj) of the control logic (22) equal to the lower signal characteristic (Fi) value, and setting an initial value of a first control parameter (C1) of the at least one control parameter (Cj) of the control logic (22) equal to or higher than the highest signal characteristic (Fi). C 1b ​ 18. The method (100) according to any one of claims 14 to 17, wherein the distribution of the at least one extracted signal feature (Fi) is based on a time series of neurosignal activity recordings lasting hours, days, weeks or months.

19. The method (100) according to any one of claims 14 to 18, wherein At least one function segment of the plurality of function segments of the control logic (22) is a function of the at least one signal feature (Fi) of the neuroactivity signal recordings, preferably a linear function of the at least one signal feature (Fi) of the neuroactivity signal recordings.

20. The method (100) according to any one of claims 14 to 19, wherein At least one stimulation parameter (A) of a stimulation signal is tuned based on a first time series of neuroactivity signal recordings collected and at least one control parameter (Cj) of the control logic (22) is tuned based on a second time series of neuroactivity signal recordings collected, wherein the first time series is shorter than the second time series.

21. The method (100) according to any one of claims 14 to 20, wherein The initialization logic is an initialization and adjustment logic (23’), and the processing module (18) is further configured to process neuroactivity signal recordings recorded by the acquisition module (20) based on the initialization and adjustment logic (23’), and to tune at least one control parameter (Cj) of the control logic (22) based on at least one neuroactivity signal recording processed according to the initialization and adjustment logic (23’), and wherein the initialization and adjustment logic (23') is a Bollinger band calculator, wherein the first control parameter (C1) of the control logic (22) is set equal to the upper Bollinger band limit and the second control parameter (C2) of the control logic (22) is set equal to the lower Bollinger band limit; or wherein the initialization and adjustment logic (23') is an unsupervised learning model establishing for each control parameter (Cj) of the control logic (22) one cluster of the neural activity signal recordings based on at least one signal feature (Fi) of the neural activity signal recordings, wherein each control parameter (Cj) corresponds to a centroid of the respective cluster; or wherein the initialization and adjustment logic (23') is a time-based fuzzy controller estimating at least one control parameter (Cj) of the control logic (22) based on at least one signal feature of the neural activity signal recordings and a relevant time slot of the day when the neural activity signal recordings were acquired.

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