Implantable closed-loop neural stimulation system and power management method thereof

Abstract

The application provides an implantable closed-loop neural stimulation system and a power consumption management method thereof. The power consumption management method comprises: determining first values of a plurality of characteristic parameters based on real-time collected electroencephalogram signals; when it is determined that the first values of the plurality of characteristic parameters meet a preset trigger condition of a neural abnormal event, switching a current working mode to a first working mode and applying an intervention stimulation pulse to the electrode; within a first time window after applying the intervention stimulation pulse, obtaining second values of the plurality of characteristic parameters; when it is determined that the first values and the second values of the plurality of characteristic parameters meet a preset condition, switching the first working mode to a second working mode, or adaptively adjusting parameters of the intervention stimulation pulse, wherein the power consumption of the first working mode is higher than that of the current working mode, and the power consumption of the second working mode is lower than that of the current working mode.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and more specifically, to an implantable closed-loop neurostimulation system and its power consumption management method. Background Technology

[0002] Implantable closed-loop neurostimulation systems automatically apply electrical stimulation to intervene when abnormal neural activity is detected by real-time monitoring of electroencephalogram (EEG) signals. This offers advantages such as timely response and high personalization. Existing technologies typically employ a basic process of event triggering, intervention stimulation, and return monitoring.

[0003] However, existing solutions require maintaining high sampling rates and continuous feature computation for extended periods to ensure high sensitivity to neural abnormalities, resulting in high power consumption. Once an intervention stimulus is triggered, the system enters a high-power mode. Due to the lack of a mechanism for quantitatively evaluating the intervention's effectiveness, even if the stimulus is ineffective, the system may repeatedly activate the high-power mode within a short period, further exacerbating the power burden.

[0004] Therefore, there is an urgent need for an implantable closed-loop neurostimulation system that can adaptively adjust power consumption. Summary of the Invention

[0005] This application addresses the shortcomings of existing methods by proposing an implantable closed-loop neurostimulation system and its power consumption management method. The aim is to solve the problem that existing implantable systems cannot achieve adaptive power consumption adjustment during the intervention and stimulation of abnormal neural events.

[0006] In a first aspect, embodiments of this application provide an implantable closed-loop neurostimulation system, comprising:

[0007] Multiple electrodes implanted in the brain;

[0008] The acquisition module is used to acquire EEG signals in real time;

[0009] The processor is configured to determine first values ​​of multiple feature parameters based on real-time acquired electroencephalogram (EEG) signals, and to determine whether the first values ​​of the multiple feature parameters satisfy preset triggering conditions for a neurological abnormal event.

[0010] The controller is configured to switch the current working mode to a first working mode and apply an intervention stimulation pulse to the electrode when it is determined that the first value of the plurality of characteristic parameters meets the preset triggering condition of a neural abnormal event, wherein the power consumption of the first working mode is higher than the power consumption of the current working mode.

[0011] The processor is further configured to acquire second values ​​of the plurality of feature parameters within a first time window after the intervention stimulation pulse is applied, and to determine whether the first and second values ​​of the plurality of feature parameters satisfy preset conditions.

[0012] The controller is further configured to switch the first working mode to the second working mode when it is determined that the first and second values ​​of the plurality of feature parameters meet preset conditions, or to adaptively adjust the parameters of the intervention stimulation pulse, wherein the power consumption of the second working mode is lower than the power consumption of the current working mode.

[0013] In a second aspect, embodiments of this application provide a power management method, wherein the power management method is performed by an implantable closed-loop neurostimulation system as described in the first aspect, the implantable closed-loop neurostimulation system comprising electrodes implanted in the cranium, and the method comprising:

[0014] Based on real-time acquired EEG signals, the first values ​​of multiple feature parameters are determined;

[0015] When it is determined that the first value of the plurality of characteristic parameters meets the preset triggering condition of the abnormal neural event, the current working mode is switched to the first working mode and an intervention stimulation pulse is applied to the electrode, wherein the power consumption of the first working mode is higher than the power consumption of the current working mode.

[0016] Within a first time window after the application of the intervention stimulus pulse, the second values ​​of the plurality of characteristic parameters are obtained;

[0017] When the first and second values ​​of the plurality of feature parameters are determined to meet the preset conditions, the first working mode is switched to the second working mode, or the parameters of the intervention stimulation pulse are adaptively adjusted, wherein the power consumption of the second working mode is lower than the power consumption of the current working mode.

[0018] Thirdly, embodiments of this application also disclose a signal processing system, including the implantable closed-loop neurostimulation system as described in the second aspect.

[0019] Fourthly, embodiments of this application also disclose a computer-readable storage medium storing a computer program that, when executed by a processor, implements one or more power management methods as described in the embodiments of the first aspect of this application.

[0020] Fifthly, embodiments of this application also disclose a computer program product, including a computer program that, when executed by a processor, implements one or more power management methods as described in the embodiments of the first aspect of this application.

[0021] The beneficial technical effects of the technical solutions provided in this application include:

[0022] The solution in this application employs a multi-level power consumption mode switching mechanism to adaptively adjust power consumption based on the intervention effect, thereby effectively reducing the average power consumption of the system and significantly extending the battery life of the implanted device. Simultaneously, based on the energy efficiency parameter values ​​calculated from the first values ​​of multiple characteristic parameters before and after the intervention stimulus, the system exits the high-power consumption mode when the intervention is confirmed to be effective; conversely, when the intervention is confirmed to be ineffective, the system adaptively adjusts the stimulus parameters (such as intensity and location) for optimization, avoiding blindly repeating stimulation and thus improving the accuracy and effectiveness of the intervention.

[0023] Furthermore, in other embodiments, the triggering conditions for neural abnormal events not only require multiple feature parameters to simultaneously meet their respective threshold conditions, but also require temporal consistency and feature coupling, thereby effectively suppressing false triggering.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0026] Figure 1 A flowchart illustrating a power management method for an implantable closed-loop neurostimulation system;

[0027] Figure 2 This is a schematic diagram of the structure of an implantable closed-loop neurostimulation system provided in an embodiment of this application;

[0028] Figure 3 This is a schematic diagram of the signal processing system provided in an embodiment of this application. Detailed Implementation

[0029] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0030] Those skilled in the art will understand that, unless specifically stated otherwise, the terms "described" and "the" as used herein may also include plural forms. It should be further understood that the term "comprising" as used in this application's specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude implementations of other features, information, data, steps, operations, elements, components, and / or combinations thereof supported by this art. It should be understood that when we say an element is "connected" or "coupled" to another element, the element may be directly connected or coupled to the other element, or it may mean that the element and the other element are connected through an intermediate element. Furthermore, "connected" or "coupled" as used herein may include wireless connections or wireless coupling. The term "and / or" as used herein refers to at least one of the items defined by the term; for example, "A and / or B" may be implemented as "A," or as "B," or as "A and B."

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0032] To facilitate understanding of the technical solution of this application, the following terms will be introduced.

[0033] An electroencephalogram (EEG) is formed by summing the postsynaptic potentials that occur synchronously among a large number of neurons during brain activity. It records the changes in electrical waves during brain activity and is a comprehensive reflection of the electrophysiological activity of brain nerve cells on the surface of the cerebral cortex or scalp. It can also be called an electroencephalogram or brainwave.

[0034] An implantable closed-loop neurostimulation system can collect electroencephalogram (EEG) signals through electrodes placed near the epileptogenic focus, perform real-time analysis, and predict or monitor epileptic seizures. When abnormalities in the patient's EEG signals are detected, electrical stimulation is automatically applied to the cortex or target brain region via electrodes to inhibit excessive synchronized firing of brain neurons, thereby suppressing epileptic seizures. This electrical stimulation, also known as an electrical stimulation signal, is the electrical signal used to stimulate the brain.

[0035] Implantable closed-loop neurostimulation systems automatically apply electrical stimulation to intervene when abnormal neural activity is detected by real-time monitoring of electroencephalogram (EEG) signals. This offers advantages such as timely response and high personalization. Existing technologies typically employ a basic process of event triggering, intervention stimulation, and return monitoring.

[0036] However, existing solutions have the following problems in power management:

[0037] 1. To ensure high sensitivity to abnormal events, the system needs to maintain a high sampling rate and continuous feature calculation for a long time, resulting in high power consumption;

[0038] 2. Once the intervention stimulus is triggered, the system enters a high power consumption mode. Due to the lack of a quantitative evaluation mechanism for the intervention effect, the following problems will occur: (1) Even if the stimulus is ineffective, the system may still repeatedly activate the high power consumption mode in a short period of time, resulting in energy waste and increased power consumption burden; (2) After the event is confirmed to have ended, it fails to switch to a lower power consumption state in time, but directly returns to the normal monitoring mode, and cannot save energy further; (3) Although some systems introduce multi-feature judgment segments, they do not combine the temporal consistency and feature coupling degree for comprehensive judgment, resulting in frequent false triggers and indirectly increasing power consumption burden.

[0039] Therefore, how to achieve refined and adaptive power consumption hierarchical management while ensuring the reliability of event detection has become an urgent problem to be solved in this field.

[0040] Therefore, this application proposes an implantable closed-loop neurostimulation system capable of adaptively adjusting power consumption. It aims to solve the following technical problems:

[0041] 1. The existing system lacks a multi-level power consumption mode switching mechanism, and cannot dynamically adjust power consumption based on the intervention effect;

[0042] 2. The existing system does not provide quantitative feedback on the intervention effect after the intervention stimulus is applied, resulting in the repeated execution of ineffective stimuli and wasting energy;

[0043] 3. Existing systems rely solely on static thresholds or simple logic combinations to determine abnormal neural events, making it difficult to distinguish between real pathological events and transient interference, resulting in unnecessary high-power activation and a high false trigger rate;

[0044] 4. Even after confirming that the neural abnormality event has ended, the system still returns to the normal monitoring mode and fails to enter the ultra-low power state to maximize battery life.

[0045] The technical solution of this application and how it solves the above-mentioned technical problems are described in detail below with specific embodiments. It should be noted that the following embodiments can be referenced, learned from, or combined with each other, and the same terms, similar features, and similar implementation steps in different embodiments will not be described again.

[0046] The implantable closed-loop neurostimulation system and its power consumption management method proposed in this application will be described in detail below with reference to the accompanying drawings.

[0047] In some embodiments, a power management method for an implantable closed-loop neurostimulation system is provided. The implantable closed-loop neurostimulation system includes multiple electrodes implanted intracranially, and the method is performed by the implantable closed-loop neurostimulation system. Figure 1 As shown, the method includes:

[0048] S1. Based on real-time acquired EEG signals, determine the first values ​​of multiple feature parameters.

[0049] In some embodiments, the multiple feature parameters include: a first feature parameter characterizing the intensity of local transient activity, a second feature parameter characterizing the phase synchronization of EEG signals from multiple channels, and a third feature parameter characterizing the complexity of the EEG signals.

[0050] Optionally, the first characteristic parameter can be the spike rate, which is the frequency of spike events occurring per unit time, reflecting the intensity of transient activity in the local cortex (or described as excitability).

[0051] Optionally, the second feature parameter can be the phase lock value (PLV) calculated based on EEG signals from multiple channels in the γ band (e.g., 30–80 Hz), which characterizes the degree of phase synchronization of EEG signals from multiple channels.

[0052] Optionally, the third feature parameter can be the differential entropy of the EEG signal based on the β band (e.g., 12–30 Hz), used to characterize the complexity of the EEG signal.

[0053] In some embodiments, the system can acquire 8 channels of EEG signals at a sampling rate of 512 Hz. The value of a characteristic parameter is calculated every 200 ms. Specifically, spikes are detected by using an amplitude threshold (e.g., 4 times the noise standard deviation) and morphological constraints. The spike rate (Hz), i.e., the value of the first characteristic parameter, is obtained by counting the number of spike events and dividing by 0.2. A Hilbert transform is performed on the filtered EEG signal in the 30–80 Hz band, and the PLV of all channel pairs is calculated and averaged to obtain the value of the second characteristic parameter. The root mean square (RMS) of the EEG signal in the 30–80 Hz band is calculated, and the natural logarithm is used as an approximation of the differential entropy to obtain the value of the third characteristic parameter.

[0054] S2. When the first value of multiple characteristic parameters is determined to meet the preset triggering conditions of a neural abnormal event, the current working mode is switched to the first working mode and an intervention stimulation pulse is applied to the electrode.

[0055] Optionally, the power consumption of the first operating mode is higher than that of the current operating mode.

[0056] In some embodiments, the first operating mode can be a high-power mode, and the current operating mode is a low-power mode. Optionally, the power consumption of the high-power mode can be 10 mW, and the power consumption of the low-power mode can be 0.1 mW, but it is not limited to these.

[0057] In some embodiments, the first values ​​of multiple feature parameters satisfy preset triggering conditions for a neural abnormality event, including: the first values ​​of multiple feature parameters satisfying a first condition and a second condition.

[0058] It should be understood that in this embodiment, when the values ​​of multiple feature parameters satisfy the temporal consistency constraint (which may correspond to the first value of multiple feature parameters satisfying the first condition), and the coupling degree of multiple feature parameters (which may correspond to the first value of multiple feature parameters satisfying the second condition) also satisfies the condition, it can be determined that a neural abnormal event has occurred.

[0059] Optionally, the first value of multiple feature parameters satisfying the first condition includes: within N consecutive acquisition time windows, the first values ​​of multiple feature parameters simultaneously satisfy the following condition:

[0060] For each acquisition time window, the first value of each of the multiple feature parameters satisfies its respective threshold condition;

[0061] For N acquisition time windows, the trend of the first value of each feature parameter among multiple feature parameters conforms to its respective preset direction. Optionally, N is an integer greater than or equal to 3.

[0062] In some embodiments, it is assumed that there is continuity. N A time window, in which N ≥ 3 (preferred) N =3, 4, or 5), the length of each time window is Tw ∈[100ms, 500ms] (preferred) Tw =200ms). In each time window k ( k =1, 2, ..., N Within this range, the values ​​of multiple feature parameters calculated in real time all satisfy their corresponding threshold conditions, namely: the first feature parameter S ( k ) ≥ T 1; Second characteristic parameter P ( k ) ≤ T 2; Third characteristic parameter D ( k ) ≥ T 3. At the same time, based on this N The feature sequences of each time window are used to calculate the changing trend of each feature parameter, and the changing trend of each feature parameter is consistent with the preset direction, that is: for the first feature parameter S ,based on N The values ​​of each time window are linearly fitted, and the slope is... α S >0 (i.e., showing an upward trend); for the second characteristic parameter P ,based on N The values ​​of each time window are linearly fitted, and the slope is... α P<0 (i.e., showing a downward trend); for the third characteristic parameter D ,based on N The values ​​of each time window are linearly fitted, and the slope is... α D If the value is >0 (i.e., it shows an upward trend), then it can be determined that the values ​​of multiple characteristic parameters satisfy the temporal consistency constraint.

[0063] Optionally, the slope can be calculated using the least squares method on the sequence { S (1), S (2),..., S ( N The time window number is obtained by performing a first-order linear fit. k =1,2,..., N .

[0064] Example 1: Assume N =4, Tw =200ms, the system collected the following characteristic parameter values ​​within the time range of t = 0~800 ms, as shown in Table 1 below:

[0065]

[0066] Table 1

[0067] Assuming a threshold: T 1 = 5.0, T 2 = 0.30, T 3 = 1.20. Since S = 4.8 < 5.0 and P = 0.32 > 0.30 for time window 1, the threshold condition is not met. Therefore, the transmission of stimulation pulses and the switching of working modes are not triggered.

[0068] Example 2: Assume N =4, Tw =200ms, the system collected the following characteristic parameter values ​​within the time range of t = 0~800 ms, as shown in Table 2 below:

[0069]

[0070] Table 2

[0071] Assuming a threshold: T 1 = 5.0, T 2 = 0.30, T =3=1.20. S, P, and D for all time windows satisfy the corresponding threshold conditions. Simultaneously, the slope fitted to the S sequence [5.1, 5.3, 5.9, 6.4] is approximately +0.43>0; the slope fitted to the P sequence [0.28, 0.29, 0.26, 0.23] is approximately... 0.03 < 0; The slope of the fitted D sequence [1.30, 1.22, 1.28, 1.35] is approximately +0.07 > 0. Therefore, the changing trend of each feature parameter is consistent with the preset direction. Thus, it can be determined that the values ​​of multiple feature parameters satisfy the temporal consistency constraint.

[0072] In this embodiment, by determining whether the values ​​of multiple feature parameters satisfy the temporal consistency constraint, false triggering caused by transient noise and physiological artifacts can be significantly suppressed.

[0073] Optionally, the first value of the multiple feature parameters satisfying the second condition includes: the coupling degree of the multiple feature parameters is greater than or equal to a preset threshold. Optionally, the coupling degree of the multiple feature parameters is determined based on the first value of the multiple feature parameters within the current acquisition time window.

[0074] In this embodiment, the coupling degree is an instantaneous coupling index used to measure the degree of coordination among multiple feature parameters at the current moment.

[0075] Following Example 2 above, in Example 3, the coupling degree C can be calculated using the following formula based on the values ​​of the feature parameters S(4), P(4), and D(4) of the fourth time window:

[0076]

[0077] Wherein, S0, P0, and D0 can be the baseline values ​​of the corresponding characteristic parameters. Optionally, S0, P0, and D0 can be the moving averages of the corresponding characteristic parameters over the most recent preset time period (e.g., 5 minutes), used to characterize the current steady-state level of the system. It should be understood that S0, P0, and D0 are dynamically updated over time, rather than having fixed initial values.

[0078] Assuming a preset threshold of 1.4, if C ≥ 1.4, it is ultimately determined to be a neural abnormality event, triggering entry into high-power mode; otherwise, even if the first condition is met, entry into high-power mode will not be triggered. This is because the parameter characteristics may satisfy the consistency constraint condition due to non-cooperative interference.

[0079] In this embodiment, by using both timing consistency constraints and coupling threshold conditions, the high-power operating mode can be avoided from being triggered unnecessarily, thus effectively saving energy and extending the system's lifespan.

[0080] S3. Within the first time window after the application of the intervention stimulus pulse, obtain the second values ​​of multiple characteristic parameters.

[0081] In some embodiments, the first time window begins 50 to 200 ms (preferably 150 ms) after the application of the intervention stimulus pulse. Optionally, the length of the first time window can be 300 to 1000 ms, and preferably, the length of the first time window can be 500 ms.

[0082] It should be understood that, in this embodiment, the first time window can be interpreted as the efficacy assessment window. To obtain accurate assessment results, it is necessary to collect steady-state response signals that are not contaminated by stimulus artifacts. Therefore, signal collection is prohibited within 50–150 ms after the application of the intervention stimulus pulse.

[0083] It should be noted that, in this embodiment, the specific implementation process of obtaining the second values ​​of multiple feature parameters can be referred to the description of the relevant content in step S1 above. For the sake of brevity, it will not be repeated here.

[0084] S4. When the first and second values ​​of multiple characteristic parameters are determined to meet the preset conditions, the first working mode is switched to the second working mode, or the parameters of the intervention stimulation pulse are adaptively adjusted.

[0085] Optionally, the power consumption of the second operating mode is lower than that of the current operating mode.

[0086] In some embodiments, the second operating mode can be an ultra-low power mode, and the current operating mode is a low power mode. Optionally, the power consumption of the ultra-low power mode can be 10 μW, and the power consumption of the low power mode can be 0.1 mW, but it is not limited to these.

[0087] In some embodiments, the first and second values ​​of a plurality of characteristic parameters satisfy a preset condition, including: the energy efficiency parameter value determined based on the first and second values ​​of the plurality of characteristic parameters satisfies the preset condition.

[0088] Optionally, the energy efficiency parameter value is obtained by weighted geometric average of the ratio of the second value to the first value of each of the multiple characteristic parameters.

[0089] In some embodiments, energy efficiency parameter values ​​can be calculated using the following formula:

[0090]

[0091] Where S, P, and D are the second values ​​of the corresponding feature parameters, S ref P ref D ref This is the first value of the corresponding feature parameter. W S , W P , W DThese are the preset positive weighting coefficients for the corresponding feature parameters, used to reflect the degree of influence of each feature parameter on the post-intervention results.

[0092] It should be noted that this formula uses a weighted geometric mean algorithm. By multiplying the normalized values ​​(second value / first value) of each feature parameter according to their weighted exponents, it can comprehensively evaluate the overall improvement of feature parameters across multiple dimensions. Since the geometric mean is sensitive to the deterioration of any single dimension, this design ensures that the energy efficiency parameter value only increases significantly when all feature parameters across all dimensions improve synergistically, thus avoiding misjudgments caused by improvements in a single feature parameter.

[0093] It should be understood that the initial value is determined before the intervention. Optionally, if the initial value is determined before the initial intervention, it can be a reference value. This reference value can be used to update the energy efficiency parameter values.

[0094] Optionally, energy efficiency parameter values ​​can be expressed as percentages to quantify the effectiveness of the intervention.

[0095] In some embodiments, step S4 above may specifically include:

[0096] When the energy efficiency parameter value is greater than or equal to the first efficiency threshold, the first working mode will be switched to the second working mode.

[0097] When the energy efficiency parameter value is less than the first efficiency threshold, the parameters of the intervention stimulus pulse are adaptively adjusted by performing the following operations:

[0098] When the energy efficiency parameter value is less than the second efficiency threshold, the first optimization strategy is executed. The first optimization strategy includes: increasing the excitation intensity of the intervention stimulus pulse by a first amplitude, and / or switching the excitation channel of the intervention stimulus pulse to the alternative master control channel.

[0099] When the energy efficiency parameter value is greater than or equal to the second efficiency threshold and less than the first efficiency threshold, the second optimization strategy is executed. The second optimization strategy includes: increasing the excitation intensity of the intervention stimulus pulse by a second amplitude, and / or activating the adjacent auxiliary channel of the excitation channel of the intervention stimulus pulse, wherein the second amplitude is less than the first amplitude.

[0100] In some embodiments, when the energy efficiency parameter value is determined to be greater than or equal to the first efficiency threshold, the intervention can be determined to be effective. In order to avoid working in high power mode for a long time, the system can be switched to ultra-low power mode.

[0101] In some embodiments, when the energy efficiency parameter value is determined to be greater than or equal to the first efficiency threshold, the intervention can be determined to be ineffective. Therefore, an optimization strategy needs to be implemented to achieve adaptive adjustment of the parameters of the intervention stimulus pulse, and the intervention can continue based on the adjusted stimulus pulse.

[0102] In some embodiments, when it is determined that the energy efficiency parameter value is greater than or equal to a first efficiency threshold, the optimization strategy to be executed can be determined based on the threshold range in which the energy efficiency parameter value is located.

[0103] Optionally, when the energy efficiency parameter value is less than the second efficiency threshold, a first optimization strategy is executed. The first optimization strategy includes: increasing the excitation intensity of the intervention stimulus pulse by a first amplitude, and / or switching the excitation channel of the intervention stimulus pulse to an alternative master control channel.

[0104] Optionally, when the energy efficiency parameter value is greater than or equal to the second efficiency threshold and less than the first efficiency threshold, a second optimization strategy is executed. The second optimization strategy includes: increasing the excitation intensity of the intervention stimulus pulse by a second amplitude, and / or activating an adjacent auxiliary channel of the excitation channel of the intervention stimulus pulse. The second amplitude is less than the first amplitude.

[0105] It should be understood that, in this embodiment, the parameters of the intervention stimulation pulse may include, but are not limited to, the stimulation intensity and stimulation channel (or described as the location of stimulation), etc. Optionally, the stimulation intensity may include, but is not limited to, current amplitude, voltage amplitude, pulse energy, etc. Optionally, the stimulation channel may be understood as selecting an activated output channel or electrode combination from multiple channels.

[0106] In one embodiment, assuming a first energy efficiency threshold Th1 = 60% and a second energy efficiency threshold Th2 = 30%, the calculated energy efficiency parameter value after the first round of intervention is 25%, which is lower than Th2. Therefore, the intervention is deemed a failure (or ineffective), and the first optimization strategy is executed: increasing the stimulation current from 1.0 mA to 1.2 mA (i.e., the first amplitude ΔI1 = +20%), and switching the excitation channel from Channel 5 to the preset alternative master control channel Channel 8.

[0107] If the energy efficiency parameter value calculated after the second round of intervention is 45%, which is in the range of [30%, 60%), then the second optimization strategy is executed: the current is slightly increased to 1.26 mA (i.e., the second amplitude ΔI2 = +5%), and Channel 8 and its adjacent channels Channel 7 and Channel 9 are activated at the same time to form a three-channel synergistic excitation.

[0108] If the energy efficiency parameter value calculated after the third round of intervention is 68% ≥ Th1, then the intervention is considered successful (or effective). The high power consumption mode is turned off, and the ultra-low power consumption mode is entered (e.g., power consumption <15μW) until the next neural abnormal event is detected.

[0109] In some embodiments, when the energy efficiency parameter value is greater than or equal to the first efficiency threshold, switching the first operating mode to the second operating mode includes: applying two pulses to the electrode when the energy efficiency parameter value is greater than or equal to the first efficiency threshold.

[0110] Within the second time window, monitor whether the value of each of the multiple feature parameters exceeds its respective safety threshold.

[0111] When the value of each characteristic parameter does not exceed its respective safety threshold within the second time window, the control electrical stimulation pulse stops output to enter the second working mode.

[0112] Optionally, the intensity of the two pulses decreases sequentially based on the excitation intensity of the intervention stimulus pulse.

[0113] In some embodiments, the security threshold corresponding to the first feature parameter S is less than its preset threshold. For example, the preset threshold of the first feature parameter S is T1, and its security threshold can be 0.7×T1, but it is not limited to this.

[0114] In some embodiments, the security thresholds for the second feature parameter P and the third feature parameter D are determined based on their respective reference values ​​(which can be the first value). For example, the security threshold for the second feature parameter P can be 0.9 × P. ref However, this is not the only one. For example, the safety threshold for the third characteristic parameter D can be 1.1 × D. ref However, it is not limited to this.

[0115] In some embodiments, when the energy efficiency parameter value is ≥ a first energy efficiency threshold (e.g., 60%), the system does not immediately shut off the stimulus output. Instead, it applies two decaying pulses with linearly decreasing excitation intensity (e.g., the first decaying pulse intensity is 70% of the final intensity of the main intervention stimulus pulse, and the second is 30%). Subsequently, a confirmation window (i.e., a second time window) lasting 500 ms is initiated, during which the values ​​of the first characteristic parameter S, the second characteristic parameter P, and the third characteristic parameter D are continuously monitored. If, within the confirmation window, S is consistently below 0.7 × T1 and P is consistently above 0.9 × P... ref D is always lower than 1.1 × D ref If the system is deemed stable, the stimulation circuit is completely shut down, and the system enters an ultra-low power mode (e.g., current <15 μA). If any characteristic parameter fails to meet the above conditions, the current "valid" determination is cancelled, the low power mode is maintained, and preparation is made for the next detection of neural abnormal events.

[0116] In some embodiments, when the energy efficiency parameter value is less than a first efficiency threshold, the above method may further include:

[0117] When the first optimization strategy or the second optimization strategy is executed a preset number of times, the first working mode is switched to the second working mode; or,

[0118] When the energy efficiency parameter value is less than the first efficiency threshold after the first or second optimization strategy has been executed a preset number of times, the first working mode will be switched to the second working mode and an external alarm will be triggered. The energy efficiency parameter value will be updated once after each optimization strategy is executed.

[0119] In some embodiments, when the energy efficiency parameter value is less than the second efficiency threshold, a first optimization strategy is executed. If the number of times the first optimization strategy is executed reaches a preset number, the first operating mode is switched to the second operating mode. Optionally, when the energy efficiency parameter value is less than the second efficiency threshold and the number of times the first optimization strategy is executed reaches a preset number, an ultra-low power consumption mode is entered.

[0120] In some embodiments, when the energy efficiency parameter value is greater than or equal to the second efficiency threshold and less than the first efficiency threshold, a second optimization strategy is executed. If the number of times the second optimization strategy is executed reaches a preset number, the first operating mode is switched to the second operating mode. Optionally, when the energy efficiency parameter value is greater than or equal to the second efficiency threshold and less than the first efficiency threshold, and the number of times the second optimization strategy is executed reaches a preset number, an ultra-low power consumption mode is entered.

[0121] In some embodiments, when the energy efficiency parameter value is less than a second efficiency threshold, a first optimization strategy is executed. If, after executing the first optimization strategy a preset number of times, the energy efficiency parameter value is still less than the first efficiency threshold, the first operating mode is switched to the second operating mode. Optionally, when the energy efficiency parameter value is less than the second efficiency threshold, and after executing the first optimization strategy a preset number of times, the energy efficiency parameter value is still less than the first efficiency threshold, the system enters an ultra-low power consumption mode.

[0122] In some embodiments, when the energy efficiency parameter value is greater than or equal to the second efficiency threshold and less than the first efficiency threshold, a second optimization strategy is executed. If, after executing the second optimization strategy a preset number of times, the energy efficiency parameter value is less than the first efficiency threshold, the first operating mode is switched to the second operating mode. Optionally, when the energy efficiency parameter value is greater than or equal to the second efficiency threshold and less than the first efficiency threshold, and after executing the second optimization strategy a preset number of times, the energy efficiency parameter value is less than the first efficiency threshold, an ultra-low power consumption mode is entered.

[0123] In the above embodiments, the energy efficiency parameter values ​​are updated once after each execution of the optimization strategy. Specifically, the updated energy efficiency parameter values ​​are obtained by taking a weighted geometric average of the ratios of the S, P, and D values ​​obtained after each execution of the optimization strategy to the corresponding reference values.

[0124] In some embodiments, the above method may further include:

[0125] When it is determined that the first value of multiple characteristic parameters does not meet the preset triggering conditions of the abnormal neural event, the current working mode is maintained, and the second working mode is automatically entered after a preset time.

[0126] In some embodiments, when the values ​​of multiple feature parameters do not meet the timing consistency constraint (which may correspond to the first value of multiple feature parameters meeting the first condition), the low-power mode is maintained, no intervention stimulus pulse is applied, and the ultra-low power mode is automatically entered after a preset duration.

[0127] In some embodiments, when the values ​​of multiple feature parameters satisfy the timing consistency constraint, but the coupling degree of multiple feature parameters (which may correspond to the first value of multiple feature parameters satisfying the second condition) does not satisfy the condition, the low-power mode is maintained, no intervention stimulus pulse is applied, and the ultra-low power mode is automatically entered after a preset duration.

[0128] In some embodiments, when the values ​​of multiple feature parameters do not meet the timing consistency constraint and the coupling degree of multiple feature parameters does not meet the condition, the low-power mode is maintained, no intervention stimulus pulse is applied, and the ultra-low power mode is automatically entered after a preset duration.

[0129] In the above embodiments, the preset duration can be 5 to 30 minutes, but is not limited to this.

[0130] In the above embodiments, the low-power mode is maintained when the triggering conditions of the neural abnormal event are not met, and the system automatically switches to the ultra-low power mode after a preset time. This can effectively avoid the activation of invalid high power consumption, significantly reduce the average power consumption of the system, thereby extending the system's battery life, while not affecting the detection sensitivity of the neural abnormal event.

[0131] In summary, the embodiments of this application provide a method for adaptively adjusting system power consumption, which can achieve the following technical effects:

[0132] 1. During periods without neurological abnormalities, low-power monitoring is maintained (i.e., normal monitoring mode). After confirming that the neurological abnormality has been effectively terminated, it automatically enters the second working mode (i.e., ultra-low power state), which consumes less power than the normal monitoring mode. This effectively reduces the average power consumption of the system and significantly extends the battery life of the implanted device.

[0133] 2. The energy efficiency parameter value is calculated based on the first value of multiple characteristic parameters before the intervention stimulus and the second value of the same multiple characteristic parameters after the intervention stimulus. The high power consumption mode is only exited when the intervention is confirmed to be effective. If the intervention is confirmed to be ineffective, the stimulus parameters (such as intensity and location) are adaptively adjusted for optimization to avoid blindly repeating the stimulus, thereby improving the accuracy and effectiveness of the intervention.

[0134] 3. The triggering conditions for abnormal neural events not only require multiple feature parameters to simultaneously meet their respective threshold conditions, but also require temporal consistency and feature coupling, thereby effectively suppressing false triggering.

[0135] 4. By using a multi-level power consumption mode switching mechanism, the power consumption can be dynamically adjusted based on the intervention effect.

[0136] Based on the same inventive concept, embodiments of this application provide an implantable closed-loop neurostimulation system, such as... Figure 2 As shown, the implantable closed-loop neurostimulation system 10 includes: multiple electrodes 11 implanted in the cranium, a data acquisition module 12, a processor 13, a controller 14, and a signal output circuit 15. The data acquisition module 12 and the signal output circuit 15 are respectively connected to the multiple electrodes 11. The data acquisition module 12 is also connected to the processor 13, and the processor 13 and the signal output circuit 15 are also respectively connected to the controller 14.

[0137] In some embodiments, the acquisition module 12 is used to acquire EEG signals in real time, and the processor 13 is used to determine first values ​​of multiple feature parameters based on the EEG signals acquired in real time by the acquisition module 12, and to determine whether the first values ​​of the multiple feature parameters meet preset triggering conditions for a neurological abnormality event. The controller 14 is used to switch the current operating mode to a first operating mode and control the signal output circuit 15 to apply intervention stimulation pulses to the electrode 11 when the processor 13 determines that the first values ​​of the multiple feature parameters meet the preset triggering conditions for a neurological abnormality event. Optionally, the power consumption of the first operating mode is higher than that of the current operating mode.

[0138] The processor 13 is further configured to acquire second values ​​of multiple characteristic parameters within a first time window after the signal output circuit 15 applies an intervention stimulation pulse to the electrode 11, and to determine whether the first and second values ​​of the multiple characteristic parameters meet preset conditions. The controller 14 is further configured to switch the first operating mode to the second operating mode when the processor 13 determines that the first and second values ​​of the multiple characteristic parameters meet the preset conditions, or to adaptively adjust the parameters of the intervention stimulation pulse. Optionally, the power consumption of the second operating mode is lower than that of the current operating mode.

[0139] In some embodiments, the first values ​​of multiple feature parameters satisfy preset triggering conditions for a neural abnormality event, including: the first values ​​of multiple feature parameters satisfying a first condition and a second condition.

[0140] Optionally, the first value of multiple feature parameters satisfying the first condition includes: within N consecutive acquisition time windows, the first values ​​of multiple feature parameters simultaneously satisfy the following condition:

[0141] For each acquisition time window, the first value of each of the multiple feature parameters satisfies its respective threshold condition;

[0142] For N acquisition time windows, the changing trend of the first value of each feature parameter in multiple feature parameters conforms to their respective preset direction, where N is an integer greater than or equal to 3;

[0143] Optionally, the first value of multiple feature parameters satisfying the second condition includes: the coupling degree of multiple feature parameters is greater than or equal to a preset threshold, and the coupling degree of multiple feature parameters is determined based on the first value of multiple feature parameters within the current acquisition time window.

[0144] In some embodiments, the first and second values ​​of multiple feature parameters satisfy preset conditions, including:

[0145] The energy efficiency parameter value determined based on the first and second values ​​of multiple characteristic parameters meets the preset conditions.

[0146] Optionally, the energy efficiency parameter value is obtained by weighted geometric mean of the ratio of the second value to the first value of each of the multiple characteristic parameters.

[0147] In some embodiments, the controller 14 is specifically used for:

[0148] When the energy efficiency parameter value is greater than or equal to the first efficiency threshold, the first operating mode is switched to the second operating mode; or,

[0149] When the energy efficiency parameter value is less than the first efficiency threshold, the parameters of the intervention stimulus pulse are adaptively adjusted by performing the following operations:

[0150] When the energy efficiency parameter value is less than the second efficiency threshold, a first optimization strategy is executed. The first optimization strategy includes: increasing the excitation intensity of the intervention stimulus pulse by a first amplitude, and / or switching the excitation channel of the intervention stimulus pulse to the alternative master control channel; or...

[0151] When the energy efficiency parameter value is greater than or equal to the second efficiency threshold and less than the first efficiency threshold, the second optimization strategy is executed. The second optimization strategy includes: increasing the excitation intensity of the intervention stimulus pulse by a second amplitude, and / or activating the adjacent auxiliary channel of the excitation channel of the intervention stimulus pulse, wherein the second amplitude is less than the first amplitude.

[0152] In some embodiments, the controller 14 is specifically used for:

[0153] When the energy efficiency parameter value is greater than or equal to the first efficiency threshold, the control signal output circuit applies two pulses to the electrode. The intensity of the two pulses decreases sequentially based on the excitation intensity of the intervention stimulus pulse.

[0154] Within the second time window, monitor whether the value of each of the multiple feature parameters exceeds its respective safety threshold.

[0155] When the value of each characteristic parameter does not exceed its respective safety threshold within the second time window, the control signal output circuit 15 stops outputting electrical stimulation pulses to enter the second working mode.

[0156] In some embodiments, when the energy efficiency parameter value is less than a first efficiency threshold, the controller 14 is further configured to:

[0157] When the first optimization strategy or the second optimization strategy is executed a preset number of times, the first working mode is switched to the second working mode; or,

[0158] After executing the first or second optimization strategy a preset number of times, if the energy efficiency parameter value is less than the first efficiency threshold, the first working mode will be switched to the second working mode, and an external alarm will be triggered.

[0159] Optionally, the energy efficiency parameter values ​​are updated after each optimization strategy is executed.

[0160] In some embodiments, the multiple feature parameters include: a first feature parameter characterizing the intensity of local transient activity, a second feature parameter characterizing the phase synchronization of EEG signals from multiple channels, and a third feature parameter characterizing the complexity of the EEG signals.

[0161] In some embodiments, the controller 14 is further configured to: when the processor 13 determines that the first value of a plurality of characteristic parameters does not meet the preset triggering conditions of a neural abnormal event, determine to maintain the current working mode and automatically enter the second working mode after a preset duration.

[0162] The implantable closed-loop neurostimulation system provided in this application embodiment can achieve the above-mentioned... Figure 1 The various processes implemented in the method embodiments shown will not be described again here to avoid repetition.

[0163] The implantable closed-loop neurostimulation system provided in this application achieves adaptive power consumption adjustment based on intervention effects through a multi-level power consumption mode switching mechanism, thereby effectively reducing the average power consumption of the system and significantly extending the battery life of the implanted device. Simultaneously, based on the energy efficiency parameter values ​​calculated from the first values ​​of multiple characteristic parameters before and after intervention stimulation, the system exits the high-power consumption mode when the intervention is confirmed to be effective; conversely, when the intervention is confirmed to be ineffective, it adaptively adjusts stimulation parameters (such as intensity and location) for optimization, avoiding blindly repeating stimulation, thus improving the accuracy and effectiveness of the intervention.

[0164] The implantable closed-loop neurostimulation system of this application embodiment can execute the power management method provided in this application embodiment. The implementation principle is similar. The actions performed by each module and unit in the implantable closed-loop neurostimulation system in each embodiment of this application correspond to the steps in the power management method in each embodiment of this application. For detailed functional descriptions of each module of the implantable closed-loop neurostimulation system, please refer to the descriptions in the corresponding power management methods shown above. They will not be repeated here.

[0165] Based on the same principles as the methods shown in the embodiments of this application, the embodiments of this application also provide a signal processing system, which includes the implantable closed-loop neurostimulation system provided in the above embodiments.

[0166] In an alternative embodiment, a signal processing system, such as Figure 3 As shown, Figure 3 The signal processing system 20 shown includes a processor 21 and a memory 23. The processor 21 is communicatively connected to the memory 23, for example, via a bus 22. Optionally, the signal processing system 20 may also include a transceiver 24, which can be used for data interaction between the signal processing system and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 24 is not limited to one type, and the structure of this signal processing system 20 does not constitute a limitation on the embodiments of this application.

[0167] Processor 21 may be a CPU (Central Processing Unit), general-purpose processor, DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof, including the chip or implantable closed-loop neurostimulation system described in any of the above embodiments. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 21 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of DSP and microprocessor, etc.

[0168] Bus 22 may include a pathway for transmitting information between the aforementioned components. Bus 22 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 22 may be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0169] The memory 23 may be a ROM (Read-Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read-Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0170] The memory 23 is used to store computer programs that execute the embodiments of this application, and the execution is controlled by the processor 21. The processor 21 is used to execute the computer programs stored in the memory 23 to implement the steps shown in the foregoing method embodiments.

[0171] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the steps and corresponding content of the aforementioned method embodiments.

[0172] This application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0173] In the description of this application, the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate directions or positional relationships based on the exemplary directions or positional relationships shown in the accompanying drawings. They are used to facilitate the description or simplification of the embodiments of this application and are not intended to indicate or imply that the device or component referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0174] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0175] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0176] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0177] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application, without departing from the technical concept of this application, also fall within the protection scope of the embodiments of this application.

Claims

1. An implantable closed-loop neurostimulation system, characterized in that, The implantable closed-loop neurostimulation system includes: Multiple electrodes implanted in the brain; The acquisition module is used to acquire EEG signals in real time; The processor is configured to determine first values ​​of multiple feature parameters based on real-time acquired electroencephalogram (EEG) signals, and to determine whether the first values ​​of the multiple feature parameters satisfy preset triggering conditions for a neurological abnormal event. The controller is configured to switch the current working mode to a first working mode and control the signal output circuit to apply an intervention stimulation pulse to the electrode when the first value of the plurality of characteristic parameters is determined to meet the preset triggering conditions of a neural abnormal event, wherein the power consumption of the first working mode is higher than that of the current working mode. The processor is further configured to acquire second values ​​of the plurality of feature parameters within a first time window after the intervention stimulation pulse is applied, and to determine whether the first and second values ​​of the plurality of feature parameters satisfy preset conditions. The controller is further configured to switch the first working mode to the second working mode when it is determined that the first value and the second value of the plurality of feature parameters meet the preset conditions, or to adaptively adjust the parameters of the intervention stimulation pulse, wherein the power consumption of the second working mode is lower than the power consumption of the current working mode. The multiple feature parameters include: a first feature parameter characterizing the intensity of local transient activity, a second feature parameter characterizing the phase synchronization degree of EEG signals from multiple channels, and a third feature parameter characterizing the complexity of the EEG signals. The first characteristic parameter is the spike rate; The second characteristic parameter is the phase lock value calculated based on EEG signals from multiple channels in the γ band; The third characteristic parameter is the differential entropy calculated based on the β-band EEG signal.

2. The implantable closed-loop neurostimulation system according to claim 1, characterized in that, The first value of the plurality of feature parameters satisfies the preset triggering conditions for a neural abnormal event, including: the first value of the plurality of feature parameters satisfies a first condition and a second condition; Wherein, the first value of the plurality of feature parameters satisfies the first condition including: within N consecutive acquisition time windows, the first value of the plurality of feature parameters simultaneously satisfies the following condition: For each acquisition time window, the first value of each of the plurality of feature parameters satisfies its respective threshold condition; For the N acquisition time windows, the changing trend of the first value of each of the multiple feature parameters conforms to their respective preset direction, where N is an integer greater than or equal to 3; The first value of the plurality of feature parameters satisfies the second condition including: the coupling degree of the plurality of feature parameters is greater than or equal to a preset threshold, and the coupling degree of the plurality of feature parameters is determined based on the first value of the plurality of feature parameters within the current acquisition time window.

3. The implantable closed-loop neurostimulation system according to claim 1, characterized in that, The first and second values ​​of the plurality of feature parameters satisfy a preset condition, including: the energy efficiency parameter value determined based on the first and second values ​​of the plurality of feature parameters satisfies the preset condition; The energy efficiency parameter value is obtained by weighted geometric average of the ratio of the second value to the first value of each of the plurality of characteristic parameters.

4. The implantable closed-loop neurostimulation system according to claim 3, characterized in that, The controller is specifically used for: When the energy efficiency parameter value is greater than or equal to the first efficiency threshold, the first working mode is switched to the second working mode; When the energy efficiency parameter value is less than the first efficiency threshold, the parameters of the intervention stimulus pulse are adaptively adjusted by performing the following operations: When the energy efficiency parameter value is less than the second efficiency threshold, a first optimization strategy is executed. The first optimization strategy includes: increasing the excitation intensity of the intervention stimulus pulse by a first amplitude, and / or switching the excitation channel of the intervention stimulus pulse to the alternative master control channel. When the energy efficiency parameter value is greater than or equal to the second efficiency threshold and less than the first efficiency threshold, a second optimization strategy is executed. The second optimization strategy includes: increasing the excitation intensity of the intervention stimulus pulse by a second amplitude, and / or activating the adjacent auxiliary channel of the excitation channel of the intervention stimulus pulse, wherein the second amplitude is less than the first amplitude.

5. The implantable closed-loop neurostimulation system according to claim 3, characterized in that, The controller is specifically used for: When the energy efficiency parameter value is greater than or equal to the first efficiency threshold, the signal output circuit is controlled to apply two pulses to the electrode, and the intensity of the two pulses decreases sequentially based on the excitation intensity of the intervention stimulation pulse. Within the second time window, monitor whether the value of each of the multiple feature parameters exceeds its respective safety threshold. When the value of each characteristic parameter does not exceed its respective safety threshold within the second time window, the signal output circuit is controlled to stop outputting electrical stimulation pulses to enter the second working mode.

6. The implantable closed-loop neurostimulation system according to claim 4, characterized in that, When the energy efficiency parameter value is less than the first efficiency threshold, the controller is further configured to: When the number of times the first optimization strategy or the second optimization strategy is executed reaches a preset number, the first working mode is switched to the second working mode; or, When the energy efficiency parameter value is less than the first efficiency threshold after executing the first optimization strategy or the second optimization strategy a preset number of times, the first working mode is switched to the second working mode and an external alarm is triggered. The energy efficiency parameter value is updated once after each execution of the optimization strategy.

7. The implantable closed-loop neurostimulation system according to any one of claims 1-6, characterized in that, The controller is further configured to: when it is determined that the first value of the plurality of feature parameters does not meet the preset triggering conditions of a neural abnormal event, determine to maintain the current working mode, and automatically enter the second working mode after a preset time.

8. A signal processing system, characterized in that, Including the implantable closed-loop neurostimulation system as described in any one of claims 1 to 7.

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