Neurological disease treatment device and control method for neurological disease 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] The present disclosure generally relates to the field of treatment of neurological diseases, and in particular to an adaptive stimulation-based neurological disease treatment apparatus and a method for controlling such an apparatus. [[Background Art]]

[0002] Invasive electrical stimulation is currently a therapeutic option for a variety of neurological diseases including movement disorders, mental illnesses, and pain. Examples of invasive brain stimulation apparatuses include deep brain stimulation (DBS) systems mainly used for treating Parkinson's disease, dystonia, essential tremor, epilepsy, and obsessive-compulsive disorder, and spinal cord stimulation (SCS) apparatuses used for pain management. Deep brain stimulation (DBS) systems have many advantages and are therefore used in various industries including medical diagnosis and medical treatment.

[0003] For example, deep brain stimulation can apply electrical stimulation to nerve structures in a patient's central nervous system to regulate nerve activity. Conventional deep brain stimulation is in many cases programmed by a physician for predefined stimulation settings, which are kept constant over time. Considering that stimulation is usually composed of a rectangular electric pulse train having amplitude, pulse width and pulse frequency, the stimulation settings set the value of at least one of the aforementioned 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 properly up-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 scales and assessments. Furthermore, pain sensation changes over time.

[0004] For such patients, adaptive, patient-specific stimulation that compensates for symptom fluctuations by adjusting stimulation settings in real time based on neurophysiological control variables is effective. However, the effectiveness of adaptive techniques is tightly dependent on the stability of neurophysiological signals, which can be influenced by electrode-tissue interface degradation, relative position, and 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 these limitations, maintaining the robustness and effectiveness of adaptive control over the long term remains a challenge for known adaptive deep brain stimulators. The same applies to spinal cord stimulators. Therefore, there is a need for novel and improved devices for the adaptive treatment of neurological disorders, as well as methods for controlling such devices. [Overview of the project]

[0006] The applicant aimed to overcome the above-mentioned problems, particularly to eliminate the need for initialization and long-term adjustment of devices for adaptive treatment of neurological disorders in order to set up and maintain patient-specific treatment that provides optimized stimulation over the long term.

[0007] Within the scope of the above-mentioned problem, the applicant aimed to devise a device that can stimulate nerve tissue according to a control logic, collect nerve signals generated in response to the stimulation, adjust stimulation parameters based on the collected nerve signals, and adjust the control logic.

[0008] Accordingly, a first aspect of the present invention relates to an apparatus for the adaptive treatment of neurological diseases, the apparatus comprising: An implantable electrode configured to sense nerve activity signals and apply electrical stimulation signals, The system comprises a processing and stimulation unit connected to an implantable electrode, the processing and stimulation unit comprising at least the following: A stimulation module configured to generate a stimulus signal transmitted by an implanted electrode, wherein the stimulus signal is characterized by at least one stimulus parameter; An acquisition module configured to record neural activity signals sensed by implanted electrodes; and A processing module configured to process neural activity signal recordings recorded by an acquisition module based on a first control logic, and to adjust at least one stimulation parameter based on at least one neural activity signal recording processed according to the first control logic, wherein the first control logic is 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 of which function elements is related to the respective range of 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 to adjust at least one control parameter of the first control logic based on 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, that is, a function defined by a plurality of partial functions, where each partial function is a different function element applied to a different interval within a domain.

[0010] The applicant has discovered that by iteratively adjusting the control parameters that characterize the first control logic based on acquired neural activity signals, it is possible to both self-initialize and self-adjust the deep brain stimulator when initiating treatment, and to sustain the optimized stimulation robustly and for a long period of time.

[0011] The applicant noted that while fluctuations in a patient's symptoms occur in real time (events within a day), disease progression and changes at the electrode-tissue interface occur over longer timeframes (days, weeks, months). Therefore, the adjustment of the control parameters of the first control logic needs to be based on the slower fluctuation trends experienced by the neural activity signal, in contrast to the real-time fluctuations used to adjust the stimulation parameters.

[0012] The applicant also noted that control logic intended to drive stimulation parameters in real time based on static control parameters cannot adequately meet the requirements of long-term robustness. Therefore, the applicant realized that adjustable control parameters are necessary 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, to control logic based on piecewise functions (functions consisting of multiple distinct functional elements), i.e., functions defined by multiple subfunctions, as known in the art, where each subfunction applies to a different interval within a domain. As known in the art, the different domain intervals of piecewise control logic applied to the adaptive treatment of neurological disorders are selected and strongly depend on the patient's state with and without pharmacotherapy and / or stimulation.

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

[0015] A second aspect of the present invention relates to a method for controlling a device for the adaptive treatment of neurological disorders, the method comprising the following steps: A step of processing a neural activity signal recording obtained by an acquisition module of an apparatus for adaptive treatment of neurological diseases, based on a first control logic, wherein the first control logic is 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 functional elements, each of which functional element is related to the respective range of at least one signal feature; A step of adjusting at least one stimulation parameter of a stimulation signal based on at least one neural activity signal recording processed according to a first control logic; A step of processing the neural activity signal recording recorded by the acquisition module based on a second control logic; and A step of adjusting at least one control parameter of the first control logic based on at least one neural activity signal recording processed according to the second control logic.

[0016] Advantageously, a 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 present invention.

[0017] The present invention may have at least one of the following preferred features. The latter can be combined with each other as desired to satisfy specific implementation needs.

[0018] In general, in some modifications, the processing and stimulation unit may be further configured to define a stimulation parameter window comprising at least a minimum value (amplitude, pulse width, frequency, or a combination thereof) of the stimulation parameter Amin that produces a detectable clinical benefit to the patient, and a maximum stimulation parameter Amax (amplitude, pulse width, frequency, or a combination thereof) before causing side effects to the patient. In some embodiments, the stimulation parameter window (Amax, Amin) can be input as unadjusted parameters.

[0019] Advantageously, setting a stimulation parameter window instead of specific stimulation parameter values ​​makes the initialization phase performed by physicians easier and faster.

[0020] In some variations, at least one stimulus parameter of the stimulus 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 on which 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 much longer timeframes (days, weeks, or months) compared to fluctuations in a patient's symptoms that occur within a range of less than a day. Therefore, long-term signal changes associated with electrode-tissue interface changes and / or disease progression can be taken into account by adjusting the control parameters of the first control logic according to a second control logic that takes into account longer time series of collected neural activity signal recordings.

[0022] In some variations, at least one of the multiple functional elements of the first control logic may be a function that depends on at least one signal feature Fi (i=1,...,N) of the neural activity signal recording. At least one of the multiple functional 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 comprise, with respect to a in the first time series F1 of collected neural activity signal features, a first function in which the stimulation parameter A is proportional to the signal feature F. The first function may be associated with a first range (C2 < F1 < C1) of neural activity signal features in the first collected time series. The first control logic may also comprise a second function that defines an upper limit value Amax of the stimulation parameter A. The second function may be associated with a second range (F1 > C1) of the signal feature amount. The first control logic may further comprise a third function that defines a lower limit value Amin of the stimulation parameter A. The third function may be associated with a third range (F1 < C2) of the signal feature. The upper limit and lower limit of the stimulation parameter A can define a stimulation parameter window (Amax, Amin).

[0024] The first control logic may be as follows: [Math.]]

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

[0026] In some embodiments, the processing and stimulation unit of the implantable device may be further configured to extract spectral features within the frequency band of the neural activity signal recording recorded over a predetermined period of time. 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, 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, the at least one signal characteristic of the neural activity signal recording may be a spectral characteristic of the neural activity signal recording within the frequency band, preferably a spectral power of the neural activity signal recording within the frequency band.

[0028] Therefore, 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 associated with 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 associated with 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 associated with a third power range (P<Pmin).

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

[0030] 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. The first and second control parameters may be adjusted based on the second control logic, in particular, on a time series of the signal power P in the frequency band that is different from the time series on which the stimulus parameter A is adjusted.

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

[0032] The background activity spectrum is obtained by using the following noise function to analyze the spectrum of neural activity signals:

number

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

[0034] In a preferred embodiment, the average power of the frequency band TIFF0007923034000004.tif7165 and mean standard deviation TIFF0007923034000005.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, Bollinger Band calculations provide time-relative settings for 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 can be implemented as K-means clustering. K-means clustering can 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, 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, unsupervised learning models can be implemented as exclusive (e.g., K-means), overlapping (e.g., fuzzy K-means), hierarchical, or stochastic (e.g., Gaussian mixture models) clustering models. The number of clusters is either predefined (e.g., 2 clusters) or set during and / or after acquisition of 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 recording and the relevant time slot of the day in which the neural activity signal recording was acquired. 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 in which each neural activity signal was recorded.

[0041] In some embodiments, at least one signal feature of the neural activity signal recording may be the 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 mean values ​​of first and second groups of spectral power values, and each group of spectral power values ​​includes spectral power values ​​for neural activity signals recorded during each time period of the day. [Brief explanation of the drawing]

[0042] [Figure 1] Figure 1 is a schematic diagram of an exemplary apparatus for the adaptive treatment of neurological disorders according to a preferred embodiment of the present invention; [Figure 2] Figure 2 is a block diagram of the closed-loop control logic implemented by a method for controlling an apparatus for adaptive treatment of neurological diseases according to a preferred embodiment of the present invention; [Figure 3A] Figure 3A is a block diagram of an exemplary embodiment of a second control logic implemented by the adaptive deep brain stimulator according to the present invention; [Figure 3B]Figure 3B is a block diagram of an exemplary embodiment of a second control logic implemented by the adaptive deep brain stimulation device according to the present invention; [Figure 3C] Figure 3C is a block diagram of an exemplary embodiment of a second control logic implemented by the adaptive deep brain stimulator according to the present invention; [Figure 4A] Figure 4A is a flowchart of a method for controlling an apparatus for adaptive treatment of neurological diseases according to a first embodiment of the present invention; [Figure 4B] Figure 4B is a flowchart of a method for controlling an apparatus for adaptive treatment of neurological diseases according to a second embodiment of the present invention; [Figure 5] Figure 5 is a flowchart of the initialization routine for a method of controlling a device for adaptive treatment of neurological diseases according to the present invention; [Figure 6] Figure 6 is a graph of the spectra of nerve signals and background activity; [Figure 7] Figure 7 is a schematic diagram of an exemplary device for the adaptive treatment of neurological disorders and an associated patient-personalized controller device. [Modes for carrying out the invention]

[0043] In the figures and the following description, the same reference numeral or symbol is used to indicate components having the same function. Furthermore, for clarity of illustration, some reference numerals may not be repeated in all figures. While embodiments and variations of the present invention are expressed and described herein, it should be understood that the present invention is not intended to be limited to the specific embodiments and variations described below, but rather, that the present invention is intended to cover all modifications or alternative and equivalent embodiments that fall within the scope of protection of the present invention as defined in the claims.

[0044] Expressions such as “examples,” “etc.,” and “or” indicate non-exclusive choices without limitation unless explicitly indicated otherwise. Expressions such as “equipment” and “include” mean “equipment, or includes, but is not limited to these,” unless explicitly indicated otherwise. Furthermore, “module” as referred to throughout this document may refer to an assembly of electrical circuits and / or electrical components arranged and connected to perform one or more functions described herein, and / or a special-purpose computer programmed to perform the functions described herein.

[0045] Referring to Figure 1, one variation of a device for adaptively treating neurological disorders is shown, with the overall number 10.

[0046] In particular, the device shown in Figure 1 is suitable for adaptive deep brain stimulation, which is configured to detect biopotentials (e.g., local potentials, i.e., LFPs) from the stimulating electrode or adjacent electrode, correlate such signals with the stimulating effect, and / or adapt the stimulating 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 deliver electrical stimulation. The probe or electrical catheter 11 may include at least three metal contacts or lead wires accessible via an external connection also called an electrode 12. However, in other variations, the electrodes may not be located on the same electrical catheter (for example, 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 the adaptive treatment of neurological disorders may comprise one or more implantable probes, each probe may comprise one or more electrodes. The device 10 may also comprise connectors or probe extensions 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 providing electrical stimulation and / or acquiring neuronal activity data) are located in the distal portion, and one or more connector contacts are located in the proximal portion, with one or more wires in the probe electrically connecting the electrodes and the connector contacts. Probe 11 may consist of any number of electrodes 12, such as 1, 2, 3, 4, 5, 6, 8, 10, 12, 16, 24, 36, 48, 64, 96, and a corresponding number of connector contacts. The probe extension may have a distal portion having a connector block with a receptacle housing surrounding one or more conductive contacts, a proximal portion having a stimulator (e.g., device 10) connector contact, where each of the stimulator connector contacts 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 can consist of any number of conductive contacts, such as 1, 2, 3, 4, 5, 6, 8, 10, 12, 16, 24, 36, 48, 64, 96, 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 connect 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 are electrically connected to the conductive contacts of the probe extension, and the electrodes of the distal portion of the probe are electrically coupled to 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 a mounting mechanism for engaging or holding the proximal portion of the probe within the receptacle housing. Optionally, the probe extension may include a connector sleeve or boot made of an electrically insulating material positioned to cover at least a portion of the receptacle housing to help electrically isolate 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 its distal and proximal portions, or a diameter that varies along its length. For example, the diameter of a segment of the elongated body can 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 can reduce excessive twisting, torque, and / or bending of the wire within the elongated body of the probe extension, thereby reducing mechanical wear of the wire and / or extending the usable life of the probe extension.

[0049] The apparatus 10 for adaptive treatment of neurological disorders depicted in Figure 1 includes a probe 11 having four metal contacts or electrodes 12, but 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 stated, the apparatus 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 apparatus 10 for adaptive treatment of neurological disorders may include a first probe having a first electrode and a second probe having a second electrode. When in use, the first probe may be implanted in a first brain region and the second probe in a second brain region (e.g., in the case of bilateral stimulation). In another variation, the device 10 for the adaptive treatment of neurological disorders may include two probes, each probe may have four electrodes (8 channels in total) or eight electrodes (16 channels in total).

[0050] In one modification, the probe 11 may include multiple electrodes, the first of which are stimulating electrodes that provide electrical stimulation, and the second of which are measuring electrodes that acquire nerve activity signals. For example, the first set of electrodes (which may or may not be adjacent to each other) can be used for stimulation, and the second set of electrodes (which may or may not be adjacent to each other, or may be arranged alternately with the first set of electrodes) can be used to acquire nerve activity signals. Alternatively or additionally, the same electrode(s) can be used simultaneously or sequentially for both acquiring nerve activity signals and electrical stimulation. The DBS probe can consist of one or more cylindrical or disc-shaped electrodes with a height of approximately 0.5 mm to approximately 3 mm, e.g., approximately 1.5 mm, and a diameter of approximately 0.5 mm to approximately 2 mm, e.g., approximately 1.27 mm. In some modifications, the DBS probe may include two or more cylindrical electrodes (e.g., using 2, 4, 6, 10, 12, 15, 16, 20 electrodes, etc.). Alternatively or additionally, the DBS probe may include planar electrodes and / or sharp electrodes having a shape at least partially selected based on the target neural structure or brain region. The distance between the two electrodes can be from about 0.25 mm to about 2 mm, for example, about 0.5 mm, and optionally, an insulator can be placed between the two electrodes and / or around the 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, for example, a platinum-iridium alloy.

[0051] In the embodiment shown in Figure 1, the electrode 12 is connected to a processing and stimulation unit 14, which includes three functional modules connected to each other in a feedback and interaction configuration: a stimulation module 16, a data acquisition module 20, and a processing module 18. The processing and stimulation unit 14 may be entirely contained within the implantable portion 10a of the device 10 for adaptive treatment of neurological disorders, or it may be distributed between the implantable portion 10a and the external portion 10b. In this second case, raw and / or processed neural activity signal recordings are wirelessly transmitted from the implantable portion 10a to the external portion 10b and vice versa. The external portion 10b can be implemented as a patient-personalized controller device. The implantable portion 10a and external portion 10b of the neurological disorder adaptive treatment device 10 are described in detail below and schematically illustrated in Figure 7.

[0052] In one modification, the processing and stimulation unit 14 may include 16 channels, which may be connected to two probes each having 8 electrodes, or four probes each having 4 electrodes, or eight probes each having 2 electrodes, etc. There may be fewer electrodes than channels; for example, the processing and stimulation unit 14 may be configured to accommodate 16 channels (e.g., 16 stimulation electrodes and / or LFP acquisition electrodes, etc.), but certain embodiments of the apparatus 10 or DBS system for adaptive treatment of neurological disorders may include 8 electrodes (e.g., two probes each having 4 electrodes) or 4 electrodes (e.g., a single probe with 4 electrodes).

[0053] The stimulation module 16 is employed to generate a stimulation signal and transmit it 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 a circuit configured to generate an electrical pulse having specific parameter values ​​determined by the user and / or controller, and may also include wires that transmit the electrical pulse to a probe that delivers the electrical pulse to a brain region.

[0054] In some variations, the stimulation module may include a waveform generator (e.g., a pulse generator, a 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, but are not limited to, electrical stimulation parameter data, which includes, electrically, stimulation amplitude, pulse width, pulse frequency, duty cycle, and / or specific probes and / or electrodes to which electrical stimulation should be supplied according to the specified parameters. The current controller may be configured to set the electrical stimulation amplitude specified by the command signals, and / or the waveform generator may be configured to generate current or voltage pulses having the pulse width and / or pulse frequency specified by the command signals. The multiplexer may be configured to electrically connect the probes and / or electrodes specified by the command signals to the current controller and / or waveform generator. In some variations, the multiplexer may include a multiplexer array configured according to command signals from the main processor so that electrical pulses from the waveform generator are directed to selected probes and / or electrodes. The connection between the waveform generator and the electrodes can be arranged in a unipolar and / or bipolar stimulation configuration by a multiplexer. In a unipolar configuration, one or more electrodes may be connected to one or more active (e.g., positive) terminals of the waveform generator (with the return pads placed in a different location 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 modifications, the 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 12. For example, the 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 the user and / or the processing module. In some modifications, the pulse generator may form an output pulse having a specified amplitude, frequency and / or pulse width or duration value. Optionally, the pulse generator may generate a pulse sequence having two or more pulses that are 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 (e.g., any of the patterns or characteristics described herein) of the acquired neural activity signal.

[0056] The data acquisition module 20 is responsible for acquiring signals that represent cerebral activity coming from the patient's brain, such as LFP signals that may represent cerebral activity in the brain region where the probe 11 is implanted. The acquisition module 20 communicates electrically with the probe 11, which in some variations may be the same probe used to electrically stimulate a brain region. The acquisition module 20 and / or the probe 11 may be configured to acquire neural activity signals, such as local potentials (LFPs), arising from activity in a brain region adjacent to the probe 11. The acquisition module 20 may include 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 the signals acquired by the acquisition module 20. The processing module may have circuits configured to facilitate communication between the acquisition module 20 and the stimulation module 16, to coordinate signal transmission between the acquisition module 20 and the stimulation module 16, and / or to 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 it may be a separate module. In some modifications, the processing module includes circuitry configured to adjust / adjust 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 for analyzing and storing acquired neural activity signals and / or signals from the acquisition module. In some modifications, the processing module may include circuitry for adjusting the power supplied to the stimulation module in coordination with electrical stimulation parameters determined by the acquisition module and / or acquired neural activity signals. The characteristics or parameters of the electrical stimulation can be determined by the acquisition module and / or the processing module. For example, the processor of the acquisition module and / or the processing module can analyze acquired and / or stored neural activity signals to identify variations or changes in the pattern 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 parameters of the electrical stimulation in response 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 a circuit configured to charge and / or measure the remaining charge in the battery. For example, the processing module may include a rechargeable battery, an induction link for charging the battery, and an induction coil for facilitating energy transfer between an external charger and a stimulator (which can 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 the spectral power value of the acquired neural activity signal, the calculated power value may be transmitted to a processing module, the processor of the processing module may be configured to derive stimulation parameters according to the power value and to send general command signals to the pulse generator to adapt or adjust the parameters of the electrical stimulation. Optionally, the processing module may include additional submodules having circuits configured for power management, electrode impedance checking, and / or calibration of the stimulation module and / or diagnostic analysis (e.g., troubleshooting).

[0059] Returning to the acquisition module 20 in Figure 1, its main function is to directly sense the difference between potentials referencing the common electrode 17, measure the electric field fluctuations of the local biopotential, and amplify such a difference to reach a voltage level useful for the analog-to-digital conversion required for signal processing.

[0060] Therefore, the acquisition module 20 may include input ports, each connected to a different electrode 12 on the probe 11, and an electrical circuit configured to measure the electric field fluctuations of the local biopotential or local potential (LFP) based on the signals from the input ports. The electrical circuit of the acquisition module may include 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 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.

[0061] Figure 2 is a functional block diagram showing the 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 of the acquired neural activity signal, for example, spectral power values. The spectral power values ​​may be transmitted to the processing module 18, which may apply a first control logic (shown in block 22) to the received spectral power values ​​to derive stimulation parameters for adapting or adjusting the parameters of electrical stimulation.

[0062] The first control logic of block 22 may be a function that depends 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 consist of a plurality of different functions, each of which function elements relates to the respective range of at least one signal feature Fi. At least one of the plurality of function elements may be a function that depends on at least one signal feature Fi of the neural activity signal recording. At least one of the plurality of function elements may be a linear function of 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, in one implementation, 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 the first time series F1 of the collected neural activity signal features. The first function may be associated with a first range (C2<F1<C1) of the neural activity signal features in the first collected time series. 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 associated with a second range of 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 associated with a third range of signal features (F1<C2). The upper and lower limits of the stimulation parameter A can define a stimulation parameter window (Amax, Amin).

[0064] Accordingly, the first control logic may be as follows: [Formula]

[0065] In this case, the first control logic is characterized by a first control parameter C1 and a second control parameter C2 that define a first range for the first time series F1 of collected neural activity signal features, and can be adjusted based on the second control logic, particularly based on the second time series F2 of collected neural activity signal features processed in accordance with the second control logic.

[0066] For example, in one embodiment, at least one signal feature of the recorded neural activity signal 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 may be input as a non-adjustable parameter.

[0067] Therefore, the first control logic may include a first function in which the stimulation parameter A is proportional to the power of a signal in a frequency band. The first function may be associated with 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 associated with 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 associated with a third power range (P<Pmin).

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

Mathematical Expression

[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, the at least one signal characteristic can 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 locking value, modulation index, kurtosis, variation index, and the like.

[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 control parameter 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] Figures 3A to 3C show examples of a second control logic 23 that the processing module 18 can use to adjust the control parameters Pmin and Pmax that characterize the first control logic.

[0073] In the example in Figure 3A, the second control logic is implemented as a Bollinger Band calculation, where the first control parameter Pmax is set to equal the upper band limit and the second control parameter Pmin is set to equal the lower band limit. Both the upper and lower band limits can 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 over any period, such as 30 minutes, 1 hour, 1 day, or 1 week. Bollinger Bands can also be calculated based on an exponential moving average. The upper and lower limits of Bollinger Bands can be defined as the number of (positive or negative) standard deviations k from the moving average.

[0075] Average power of the frequency band TIFF0007923034000010.tif7165 and mean standard deviation TIFF0007923034000011.tif7165 may be calculated by a second control logic using sliding time periods, for example, a 24-hour sliding time period. Thus, after 24 hours, for each time step (for example, 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 Bollinger Bands can be calculated as follows:

number

number

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

[0078] In some variations, Bollinger Band calculations can be applied to control parameters of a first control logic that defines a threshold and is dynamically adjustable. The first control logic can control the on / off state of a stimulus based on a threshold of at least one signal feature of a neural activity signal recording. The threshold can be calculated as an upper or lower limit of the Bollinger Bands.

[0079] In the example in Figure 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 K-means clustering. K-means clustering 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] For example, at least one signal feature of a neural activity signal recording may be the spectral power of a frequency band 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 closest to the cluster centroid are classified into the corresponding cluster. The value of each centroid is updated after the calculated spectral power is assigned to each cluster. The control parameters Pmax and Pmin, determined in the initialization step, provide the starting values ​​for the cluster centroids and change iteratively according to the calculated spectral power values ​​added to each cluster. This allows the control parameters Pmax and Pmin to be adjusted over time based on the development of the recorded neural activity signal. Alternatively, the centroid values ​​can be initialized randomly.

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

[0082] In the example in Figure 3C, the second control logic 23 may be implemented as 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 recording and the corresponding time period on the day of the recording. The time-based fuzzy controller can group and process at least one signal feature of the recorded neural activity signals based on the time period on the day each neural activity signal was recorded.

[0083] For example, in one embodiment, signal features of neural activity signals recorded between 10:00 AM and 12:00 PM and between 2:00 PM and 4:00 PM may be grouped to belong to 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 may be grouped to belong to a second group. As an example, at least one signal feature of a neural activity signal recording may be the spectral power of a frequency band calculated for each recording of neural activity signals. 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 real-time application scenarios, 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 neural activity signals can be pre-configured.

[0084] Figure 4A shows a first exemplary method 100 for controlling an apparatus for adaptive treatment of neurological disorders according to the present invention. Method 100 comprises the step of acquiring and / or storing a recording of neuronal activity signals, such as local potentials (LFPs), for a predefined period (e.g., 1 day, 10 days, etc.). For example, in some embodiments, a recording of neuronal activity signals during the day (e.g., in daily life) can be acquired and recorded separately for the left subthalamic nucleus (STN) and the right STN in a frequency range between 5 Hz and 35 Hz. Acquisition can be performed in frequency bands of neuronal activity signals selected from low frequency bands, alpha frequency bands, beta frequency bands, and / or gamma frequencies. The frequency band can be determined by identifying a peak in the extracted spectral features and preferably defining the frequency band as the frequency range around the peak. In some modifications, the frequency band is either preset (hardcoded) or input as an unadjustable parameter.

[0085] Method 100 comprises the step of processing the acquired neural activity signal recording and extracting neural signal features in 102. For example, in one embodiment, the method can provide the extraction of spectral features within a frequency band of neural activity signal recordings recorded over a predetermined period, such as spectral power.

[0086] The extracted signal features can be transmitted to the processing module 18 in 103 to derive stimulation parameters according to the first and second control logics, as described above. Specifically, at least one stimulation parameter of the stimulation signal is adjusted based on the first control logic and the neural activity signal recording, while the neural activity signal recording is processed according to the second control logic to adjust the control parameters of the first control logic.

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

[0088] Figure 4B shows a second exemplary method 200 for controlling an apparatus for adaptive treatment of neurological disorders according to the present invention. In addition to the steps of the first exemplary method 100, method 200 includes a routine for initializing a first control logic based on collected nerve signals.

[0089] Method 200 comprises the steps of acquiring and / or saving a neural activity signal recording in 101, and further processing the acquired neural activity signal recording to extract neural signal features in 102. If the process has just started and the first control logic still needs to be initialized, i.e., if its control parameters still need to be set, the steps of acquiring and / or saving the neural activity signal recording and further processing the acquired neural activity signal recording are preferably performed without stimulation and / or medication, and in 201 and 202, the method calculates the control parameters using the neural signal features extracted from the acquired neural activity signal recording (described in detail below). Finally, adaptive stimulation is initiated in 203.

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

[0091] In 302, method 300 determines the spectral power 605 of the neural activity signal recording in a selected patient-specific frequency band. Next, in 303, the method determines the spectral power 604 of the background activity spectrum 602 in the same patient-specific frequency band. For this purpose, 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 to 304 and 305, respectively. The first control parameter Pmax is set to equal the spectral power 605 of the neural activity signal recording acquired in the absence of stimulation, calculated in a selected patient-specific frequency band, and the second control parameter Pmin is set to equal the spectral power 604 of the background activity spectrum, which is fitted to the spectrum of the neural activity signal acquired in the absence of stimulation and / or medication, calculated in a selected patient-specific frequency band.

[0093] Figure 7 is a schematic diagram illustrating an exemplary method for establishing a wireless connection between the implantable portion 10a (also referred herein as the “implantable pulse generator (IPG) device”) and the external portion 10b (also referred herein as the “patient personal controller device”) of the neurological disease adaptation 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 and 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, a wireless connection between the implanted device 10a and the patient personal controller device 10b (e.g., the charger unit of the patient personal controller device) may be established when the patient personal controller device 10b and the implanted device 10a are within a predetermined distance range (e.g., 2 centimeters to 10 centimeters and / or 1 millimeter to 1 meter, etc.) and azimuthal range. The azimuthal range may include, for example, a 5-degree rotation error range, a 10-degree rotation error range, and / or alignment of the vertical orientation of the patient personal controller device and the vertical orientation of the implanted device within such ranges. The personal controller device 10b can transmit power to the implanted device 10a, and the implanted device 10a can transmit neural activity signal recordings to the personal controller device 10b via the first wireless connection.

[0094] In the preceding description, for the sake of clarity, the application of DBS was mentioned in particular. However, the disclosed invention can also be applied to other embodiments, such as SCS for pain treatment. In the case of SCS, the acquisition module can be configured to acquire the neural activity of spinal nerves in response to electrical stimulation when electrodes are placed on the spine. The spinal cord's response to electrical stimulation is the activation of an ensemble of nerve fibers, i.e., the sum of evoked composite action potentials (ECAPs). The processing module may be configured to calculate the signal features of a set of acquired neural activity signals (e.g., spectral power of ECAPs). The spectral power values ​​may be transmitted to the processing module, and the processor of the processing module may apply first control logic to the received signal features to derive stimulation parameters for adapting or adjusting the parameters of the electrical stimulation. The processing module processor can also apply second control logic to adjust the control parameters of the first control logic, as described above. The aforementioned considerations regarding the implementation of the first and second control logics still apply.

[0095] In the foregoing description, a specific nomenclature was used for the sake of clarity and to ensure that the present invention could be fully understood. However, it will be apparent to those skilled in the art that specific details are not necessary to carry out the present invention. Therefore, the foregoing description of specific modifications of the present invention is presented for illustrative and explanatory purposes only. These are not intended to be exhaustive or to limit the present invention to the exact form disclosed, and obviously, many modifications and variations are possible in view of the above teachings. The modifications have been selected and described to illustrate the principles of the present invention and its practical applications, so that those skilled in the art can adapt and utilize the present invention and its various modifications in various ways to suit the specific intended use. The following claims and their equivalents are intended to define the scope of the present invention.

Claims

1. A device (10) for the appropriate treatment of neurological diseases, An implantable electrode (11) configured to sense nerve activity signals and apply electrical stimulation signals, The system comprises an implantable electrode (11) and a processing and stimulation unit (14) connected thereto. The processing and stimulation unit is, A stimulation module (16) configured to generate a stimulation signal transmitted by the implanted electrode (11), wherein the stimulation signal is characterized by at least one stimulation parameter, An acquisition module (20) configured to record neural activity signals sensed by the implanted electrode, A processing module (18) is configured to process the neural activity signal recording 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), wherein the first control logic is a function dependent on at least one signal feature (Fi) of the neural activity signal recording, based on at least one control parameter (Cj), and composed of a plurality of different function elements, each 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 recording 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), and It has at least Apparatus (10).

2. At least one stimulus parameter (A) of the stimulus signal is a first time series (F) of the collected neural activity signal recording. １ ) is adjusted based on the second time series (F) of the collected neural activity signal recording, and at least one control parameter (Cj) of the first control logic (22) is adjusted based on the second time series (F) of the collected neural activity signal recording. ２ ) are adjusted based on the first time series being shorter than the second time series. The apparatus (10) according to claim 1.

3. The second time series of collected neural activity signal recordings that serves as a basis for adjusting at least one control parameter (Cj) of the first control logic (22) is a spectral power time series collected over several minutes, hours, days, weeks, or months. The apparatus (10) according to claim 2.

4. The second control logic (23) is a Bollinger Band calculator, and the first control parameter (C) of the first control logic (22) １ ) is set to the upper limit of the band, and the second control parameter (C) of the first control logic (22) ２ ) is set as the lower limit of the band, or The second control logic (23) is an unsupervised learning model that establishes a cluster of the neural activity signal recording based on at least one signal feature (Fi) for each control parameter (Cj) of the first control logic (22), wherein each control parameter (Cj) corresponds to the centroid of the 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 relevant time period on the day the neural activity signal recording was acquired. The apparatus (10) according to claim 1.

5. At least one of the plurality of function elements of the first control logic (22) is a linear function that depends on the at least one signal feature (Fi) of the neural activity signal recording. The apparatus (10) according to claim 1.

6. The first control logic described above is: The first function is such that the at least one stimulus parameter (A) is proportional to the at least one signal feature (Fi), and the first function is such that the at least one signal feature has a first range (C) ２ <Fi<C １ The first function related to ) and A second function that defines the upper limit (Amax) of the at least one stimulus parameter (A), wherein the second function is a second range (Fi > C) of the at least one signal feature. １ The second function related to ) A third function that defines the lower limit (Amin) of the at least one stimulus parameter (A), wherein the third function is a third range (Fi < C) of the at least one signal feature. ２ ) including a third function related to The apparatus (10) according to claim 1.

7. The first control logic (22) is, [Number 1] The first range of the first time series of the collected neural activity signal features among the aforementioned signal features is the second time series of the collected neural activity signal features (F ２ ) which is an upper limit (C that is a control parameter of the first control logic (22) adjusted according to the second control logic (23) based on １ ) and a lower limit (C ２ ) 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, wherein the frequency band is 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 the spectral power of the neural activity signal recording within the frequency band. The apparatus (10) according to claim 1.

9. The first control logic (22) includes a first function in which the at least one stimulation parameter (A) is proportional to the spectral power (P) of the neural activity signal recording within the frequency band, and the first function is related to a first power range (Pmin < P < Pmax) which is between a minimum spectral power (Pmin) and a maximum spectral power (Pmax). The apparatus (10) according to claim 8.

10. The first control logic (22) includes a second function that defines an upper limit (Amax) of the at least one stimulus parameter (A), the second function relating to a second power range (P > Pmax) adjacent to the first power range (Pmin < P < Pmax), and a third function that defines a lower limit (Amin) of the at least one stimulus parameter (A), the third function relating to a third power range (P < Pmin) adjacent to the first power range (Pmin < P < Pmax). The apparatus (10) according to claim 9.

11. The first control logic (22) is, [Math 2] Amin and Amax are the lower and upper limits of the stimulation parameter (A), respectively, 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). The apparatus (10) according to claim 9.

12. The second control logic (23) is a Bollinger Band calculator, and the first control parameter (C) of the first control logic (22) １ ) is set to the upper limit of the band, and the second control parameter (C) of the first control logic (22) ２ ) is set as the lower limit of the band, The upper and lower band limits are the average power Mean standard deviations that are positive or negative from the value. The number (k) is such that the average power and the average standard deviation are both calculated with a sliding time period, and the average power This 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. The apparatus (10) according to claim 8.

13. The at least one control parameter (Cj) of the first control logic (22) is set to be equal to the average value of the signal features grouped by the second control logic (23) based on the time period on the day the neural activity signal recording was acquired. The apparatus (10) according to claim 4.

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

15. The processing and stimulation unit (14) is configured to define a stimulation parameter window that falls between the lower limit (Amin) and the upper limit (Amax) of the at least one stimulation parameter (A). The apparatus (10) according to claim 1.

16. A method (100) for controlling a device (10) for the adaptive treatment of neurological diseases, The processing module (18) of the apparatus (10) for adaptive treatment of neurological diseases, A step of processing a neural activity signal recording recorded by an acquisition module (20) of the apparatus (10) for adaptive treatment of neurological diseases, based on a first control logic (22), wherein the first control logic (22) is a function dependent on at least one signal feature (Fi) of the neural activity signal recording, based on at least one control parameter (Cj), and composed of a plurality of different functional elements, each of the plurality of different functional elements relating to the respective range of the at least one signal feature (Fi), A step of adjusting at least one stimulation parameter (A) of a stimulation signal based on at least one neural activity signal recording processed according to the first control logic (22), The steps include processing the neural activity signal recording recorded by the acquisition module (20) based on the second control logic (23), A step of adjusting the at least one control parameter (Cj) of the first control logic (22) based on at least one neural activity signal recording processed according to the second control logic (23); Method (100), including the 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 recordings, and 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 recordings, wherein the first time series is shorter than the second time series. The method according to claim 16 (100).

18. The second time series of collected neural activity signal recordings, which serves as a basis for adjusting the at least one control parameter (Cj) of the first control logic (22), is a power time series collected over several minutes, hours, days, weeks, or months. The method according to claim 17 (100).

19. The second control logic (23) is a Bollinger Band calculator, and the first control parameter (C) of the first control logic (22) １ ) is set to be equal to the upper limit of the band, and the second control parameter (C) of the first control logic (22) ２ ) is set equal to the lower limit of the band, or The second control logic (23) is an unsupervised learning model that establishes a cluster of the neural activity signal recording based on at least one signal feature (Fi) for each control parameter (Cj) of the first control logic (22), wherein each control parameter (Cj) corresponds to the centroid of the 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 relevant time period on the day the neural activity signal recording was acquired. The method according to claim 16 (100).

20. The step of processing the neural activity signal recording includes the processing module (18) extracting spectral features within a frequency band of the neural activity signal recording recorded over a predetermined period, wherein the frequency band is 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 the spectral power of the neural activity signal recording within the frequency band. The method according to claim 16 (100).

21. The second control logic (23) is a Bollinger Band calculator, and the first control parameter (C) of the first control logic (22) １ ) is set to be equal to the upper limit of the band, and the second control parameter (C) of the first control logic (22) ２ ) is set equal to the lower limit of the band, The upper and lower band limits are the average power Mean standard deviations that are positive or negative from the value. The number (k) is such that the average power and the average standard deviation are both calculated with a sliding time period, and the average power This 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. The method according to claim 20 (100).

22. The at least one control parameter (Cj) of the first control logic (22) is set to be equal to the average value of the signal features grouped by the second control logic (23) based on the time period on the day the neural activity signal recording was acquired. The method according to claim 19 (100).

23. The processing module (18) further includes the step of initializing the at least one control parameter (Cj) based on the at least one neural activity signal recording acquired by the acquisition module (20) The method according to claim 16 (100).

24. The first control logic (22) defines a stimulus parameter window that falls between the lower limit (Amin) and the upper limit (Amax) of the at least one stimulus parameter (A). The method according to claim 16 (100).

25. At least one of the plurality of function elements of the first control logic (22) is a linear function that depends on the at least one signal feature (Fi) of the neural activity signal recording. The method according to claim 16 (100).

26. The first control logic described above is: The at least one stimulus parameter (A) is a first function proportional to the at least one signal feature (Fi), wherein the at least one signal feature has a first range (C ２ <Fi<C １ The first function related to ) and A second function that defines the upper limit (Amax) of the at least one stimulus parameter (A), wherein the second range (Fi > C) of the at least one signal feature is defined as the upper limit (Amax) of the at least one stimulus parameter (A). １ ) and a second function related to, A third function that defines the lower limit (Amin) of the at least one stimulus parameter (A), wherein the third range (Fi < C) of the at least one signal feature is defined as the range of the at least one signal feature. ２ It comprises a third function related to ) The method according to claim 16 (100).

27. The first control logic (22) is, [Math 3] The first time series (F) of the collected neural activity signal features of the aforementioned signal features is １ The first range of ) is the second time series (F) of neural activity signal features collected according to the second control logic (23). ２ The upper limit (C) is a control parameter of the first control logic (22) that is adjusted based on the above. １ ) and lower limit (C ２ ) including The method according to claim 26 (100).

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

29. The first control logic (22) includes a second function that defines an upper limit (Amax) of the at least one stimulus parameter (A), the second function relating to a second power range (P > Pmax) adjacent to the first power range (Pmin < P < Pmax), and a third function that defines a lower limit (Amin) of the at least one stimulus parameter (A), the third function relating to a third power range (P < Pmin) adjacent to the first power range (Pmin < P < Pmax). The method according to claim 28 (100).

30. The first control logic (22) is, [Math 4] Amin and Amax are the lower and upper limits of the stimulation parameter (A), respectively, 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). The method according to claim 28 (100).

Images