Signal processing method, implantable closed-loop neural stimulation system, and storage medium

Abstract

The embodiment of the application relates to the technical field of medical devices, and discloses a signal processing method, an implantable closed-loop nerve stimulation system and a storage medium, the method comprising: monitoring an electroencephalogram signal, and generating a first control instruction when a target signal mode is identified, the first control instruction being used to instruct output of a first electric stimulation signal according to a first group of stimulation parameters; collecting an electroencephalogram response signal in a first time window after the first electric stimulation signal is output; calculating a characteristic change index in at least two dimensions according to the electroencephalogram response signal; and generating a second control instruction according to a combination of the characteristic change index in at least two dimensions and a third decision signal, the second control instruction being used to instruct output of a second electric stimulation signal according to a second group of stimulation parameters which are dynamically adjusted.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and more specifically, to a signal processing method, an implantable closed-loop neurostimulation system, and a storage medium. Background Technology

[0002] Implantable closed-loop neurostimulation systems, as advanced medical devices, have great development prospects. Currently, the operation of implantable closed-loop neurostimulation systems typically involves outputting electrical stimulation according to preset stimulation parameters when a target condition is detected.

[0003] However, while the above-mentioned working method of the implantable closed-loop neurostimulation system is logically simple and relatively safe, it has another drawback: the working method of the implantable closed-loop neurostimulation system is not flexible enough. Summary of the Invention

[0004] This application provides a signal processing method, an implantable closed-loop neurostimulation system, and a storage medium to address the problem of poor flexibility in the operation of existing implantable closed-loop neurostimulation systems.

[0005] To address the aforementioned problems, this application discloses a signal processing method applied to a signal processing system, the signal processing method comprising:

[0006] Monitor the electroencephalogram (EEG) signal, and when the EEG signal that matches the target signal pattern is identified, generate a first control command, the first control command being used to instruct the output of a first electrical stimulation signal according to a first set of stimulation parameters;

[0007] Collect the EEG response signal within the first time window after the first electrical stimulation signal is output;

[0008] Based on the EEG response signal, at least two-dimensional feature change indicators are calculated; wherein, the feature change indicators include: a first feature indicator based on the change of signal frequency domain energy in the target frequency band, a second feature indicator based on the change of signal complexity, and a third decision signal based on pattern recognition of the EEG response signal;

[0009] A second control command is generated based on a combination of the characteristic change indicators in at least two dimensions and the third decision signal. The second control command is used to instruct the output of a second electrical stimulation signal according to a dynamically adjusted second set of stimulation parameters.

[0010] This application also discloses an implantable closed-loop neurostimulation system, which includes:

[0011] The monitoring module is used to monitor electroencephalogram (EEG) signals and generate a first control command when the EEG signal that matches the target signal pattern is identified. The first control command is used to instruct the output of a first electrical stimulation signal according to a first set of stimulation parameters.

[0012] The acquisition module is used to acquire the EEG response signal within the first time window after the first electrical stimulation signal is output;

[0013] The processing module is used to receive the EEG response signal acquired by the acquisition module, and calculate feature change indicators in at least two dimensions based on the EEG response signal; wherein the feature change indicators include: a first feature indicator based on the change of signal frequency domain energy in the target frequency band, a second feature indicator based on the change of signal complexity, and a third decision signal based on the pattern recognition obtained from the EEG response signal;

[0014] The processing module is further configured to generate a second control instruction based on a combination of the feature change indicators in at least two dimensions and the third decision signal, wherein the second control instruction is configured to instruct the output of a second electrical stimulation signal according to a dynamically adjusted second set of stimulation parameters.

[0015] This application also discloses a signal processing system, including the implantable closed-loop neurostimulation system described above.

[0016] This application also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements one or more of the methods described in this application.

[0017] This application also discloses a computer program product, including a computer program that, when executed by a processor, implements one or more of the methods described in this application.

[0018] The beneficial effects of the technical solutions provided in this application are:

[0019] In this embodiment, after executing the initial output electrical stimulation signal, the signal processing system acquires the EEG response signal and calculates multi-dimensional feature change indicators, including frequency domain energy, signal complexity, and pattern recognition results, to comprehensively assess the changes in the EEG signal. Subsequently, the signal processing system makes decisions based on the combination of these multi-dimensional feature change indicators, dynamically generates the next set of stimulation parameters, and outputs a second electrical stimulation signal according to the dynamically adjusted second set of stimulation parameters via a second control command. This allows the signal processing system to operate dynamically, moving away from a preset fixed mode and improving operational flexibility. Attached Figure Description

[0020] 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:

[0021] Figure 1 A flowchart of the signal processing method provided in the embodiments of this application;

[0022] Figure 2 A schematic diagram of the structure of the implantable closed-loop neurostimulation system provided in an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of the signal processing system provided in an embodiment of this application. Detailed Implementation

[0024] 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.

[0025] Those skilled in the art will understand that, unless otherwise stated, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in the embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “multiple” refers to two or more; therefore, in the embodiments of this application, “multiple” can also be understood as “at least two.” The term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / ", unless otherwise specified, generally indicates that the related objects before and after it are in an "or" relationship.

[0026] 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.

[0027] To facilitate understanding of the technical solution of this application, the following terms will be introduced.

[0028] 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.

[0029] 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.

[0030] Currently, existing implantable closed-loop neurostimulation systems typically employ a pre-set set of fixed stimulation parameters. When electrical stimulation is required, a signal is output according to these parameters. For example, in implantable closed-loop neurostimulation systems used to treat epilepsy, ensuring these parameters achieve optimal therapeutic effects requires physicians or medical experts to carefully formulate the parameters based on the patient's overall condition and clinical presentation, combined with their extensive clinical experience. This process is time-consuming, labor-intensive, and highly dependent on the physician. However, patients' conditions vary greatly, and even for the same patient, their condition may change unpredictably over time. Therefore, existing implantable closed-loop neurostimulation systems still have significant shortcomings in their stimulation strategies, relying on fixed parameters for electrical stimulation, resulting in poor flexibility.

[0031] Therefore, this application proposes a signal processing method applied to a signal processing system. In some embodiments, the signal processing system includes an implantable closed-loop neurostimulation system, which can be used for neuroscience research, brain-computer interface research, and treatment of targeted diseases. For ease of understanding, the following uses an implantable closed-loop neurostimulation system as an example to illustrate the signal processing method. Figure 1 As shown, the signal processing method includes:

[0032] Step 101: Monitor EEG signals, and when an EEG signal that matches the target signal pattern is identified, generate a first control command. The first control command is used to instruct the output of a first electrical stimulation signal according to a first set of stimulation parameters.

[0033] In this step, the implantable closed-loop neurostimulation system can monitor electroencephalogram (EEG) signals and identify their characteristics. For example, it can identify whether the EEG signals conform to a target signal pattern. The target signal pattern can be a predefined signal pattern or signal characteristic. Taking the treatment of epilepsy with an implantable closed-loop neurostimulation system as an example, the target signal pattern can be the characteristics of epilepsy onset, and EEG signals conforming to the target signal pattern can be EEG signals that conform to the characteristics of epilepsy onset, i.e., epileptic EEG signals.

[0034] Therefore, in some embodiments, when a pathological EEG signal is detected, a first control command can be generated, and then the implantable closed-loop neurostimulation system can output a first electrical stimulation signal according to a first set of stimulation parameters.

[0035] In some embodiments, during the operation of the implantable closed-loop neurostimulation system, the system can continuously monitor the patient's electroencephalogram (EEG) signals through implanted electrodes. The system has a pre-set algorithm for recognizing pathological EEG signals, such as those based on time-frequency analysis or machine learning models, to detect EEG signals (such as spikes, sharp waves, or paroxysmal rhythm changes) that match the pathological characteristics of the target condition in real time. When the system detects a pathological EEG signal, it immediately generates a first control command and executes the first control command.

[0036] In some embodiments, the first set of stimulation parameters may include stimulation amplitude or intensity (e.g., 1mA), stimulation frequency (e.g., 10-150 Hz), pulse width (e.g., 100-500 μs), and stimulation duration (e.g., 100-500 ms). When the first control command is executed, an electrical stimulation signal will be output according to the aforementioned stimulation intensity, stimulation frequency, pulse width, and stimulation duration.

[0037] In some embodiments, the stimulation intensity, stimulation frequency, pulse width, and stimulation duration can be pre-configured based on the patient's medical history or clinical testing. By executing a first control command, the implantable closed-loop neurostimulation system is able to output a first electrical stimulation signal. For example, outputting the first electrical stimulation signal can inhibit abnormal neuronal firing.

[0038] Step 102: Collect the EEG response signal within the first time window after the first electrical stimulation signal is output.

[0039] In this step, the first time window is the time window after the first electrical stimulation signal output ends.

[0040] After the initial electrical stimulation signal output, the implantable closed-loop neurostimulation system enters the data acquisition phase. Specifically, the system acquires the EEG response signal within a first time window, which can be set to a short period (e.g., 1-30 seconds) after the stimulation ends, to ensure that the neural response after stimulation is captured. It can be understood that the EEG response signal can be the EEG signal acquired within the first time window after the initial electrical stimulation signal output. Since the EEG signal acquired at this time is the response of the nerves after receiving stimulation, it can also be called the EEG response signal.

[0041] In some embodiments, the EEG response signal can be acquired using the same electrode array or dedicated monitoring electrodes in an implantable closed-loop neurostimulation system and preprocessed (e.g., filtering, denoising, and signal amplification) to improve signal quality.

[0042] Step 103: Calculate feature change indicators in at least two dimensions based on the EEG response signal.

[0043] In this step, the feature change indicators include: a first feature indicator based on the change in signal frequency domain energy under the target frequency band, a second feature indicator based on the change in signal complexity, and a third decision signal based on pattern recognition of the EEG response signal.

[0044] The target frequency band is a predefined frequency band; for example, signals within the target frequency band can be high-frequency oscillations (HFOs) signals. In some embodiments, the first characteristic index can characterize the power change of the HFOs signal. Taking the treatment of epilepsy with an implantable circular nerve stimulation system as an example, HFOs signals can refer to EEG signals in the 80-500Hz frequency band, which are closely related to epileptic activity. For example, HFOs can be considered as an extreme manifestation of abnormal synchronous discharge of neuronal groups. In the normal state (seizure-free epilepsy), the power of HFOs signals is usually low, while during epileptic seizures, the power of HFOs signals is usually high. In some embodiments, the first parameter is defined as the rate of change of the power of the HFOs signal after stimulation relative to the baseline power before stimulation (such as the power of HFOs signals in epileptic EEG signals). For example, a power decrease of more than 50% may indicate good therapeutic effect. This first characteristic index reflects the improvement of the current seizure.

[0045] In some embodiments, the second characteristic index can characterize the change in sample entropy of the EEG signal. Sample entropy can be used to measure the complexity of a time series. In this embodiment, sample entropy is used to quantify the complexity of the EEG response signal. Continuing with the example of treating epilepsy using an in-circuit neurostimulation system, in a normal state (seizure-free epilepsy), the EEG signal is usually complex and irregular, and its sample entropy is usually high. During an epileptic seizure, the EEG signal becomes synchronous and monotonous, and its sample entropy is usually low. Therefore, in some embodiments, the second characteristic index can be used to determine the amount or rate of change of sample entropy to reflect the recovery of brain function.

[0046] In some embodiments, the third decision signal can characterize the classification result of the artificial intelligence (AI) model. For example, the AI ​​model is used to output a classification result of "continue treatment" or "stop treatment" based on the input EEG response signal. In some embodiments, the AI ​​model is a pre-trained machine learning model (such as a convolutional neural network, support vector machine, large model, etc.), whose input is the EEG response signal and whose output is a binary classification result: "continue treatment" or "stop treatment". The training process of the AI ​​model will not be described in detail in this application.

[0047] In this embodiment, multiple feature indicators can be calculated simultaneously to ensure the comprehensiveness of the evaluation. For example, in resource-constrained scenarios, the first and third feature indicators can be used preferentially, but ideally, all three feature indicators can be utilized comprehensively.

[0048] Step 104: Generate a second control command based on the combination of feature change indicators in at least two dimensions and a third decision signal. The second control command is used to instruct the output of a second electrical stimulation signal according to a dynamically adjusted second set of stimulation parameters.

[0049] In this step, the feature change indicators in at least two dimensions may include a first feature indicator and a second feature indicator. Therefore, a second control command can be generated based on at least two of the first feature indicator, the second feature indicator, and the third decision signal.

[0050] Continuing with the example of treating epilepsy using an implantable closed-loop neurostimulation system, a treatment strategy for the current seizure can be determined based on characteristic changes in at least two dimensions and a third decision signal. This treatment strategy includes a first strategy instructing treatment to cease and a second strategy instructing dynamic adjustment of stimulation parameters and outputting a second electrical stimulation signal. The implantable closed-loop neurostimulation system is then controlled according to the current seizure treatment strategy.

[0051] Understandably, the first strategy is to instruct the patient to stop treatment. For example, if multiple indicators show good efficacy (e.g., HFOs power decreases beyond a threshold, sample entropy increases, and the AI ​​model outputs "stop treatment"), then the first strategy is chosen to stop further stimulation to avoid overtreatment.

[0052] The second strategy involves dynamically adjusting stimulation parameters and outputting a second electrical stimulus. For example, if any characteristic indicator suggests insufficient efficacy (e.g., HFOs power does not decrease, sample entropy decreases, or the AI ​​model outputs "continue treatment"), then the second strategy is selected.

[0053] In some embodiments, during the dynamic adjustment of stimulation parameters, if the HFOs power changes little, the stimulation intensity can be increased; if the sample entropy does not change significantly, the stimulation frequency can be adjusted.

[0054] In some embodiments, during the execution of the second control command, the implantable closed-loop neurostimulation system outputs a second electrical stimulation signal based on the adjusted stimulation parameters (the second set of stimulation parameters). Subsequently, steps 102 to 104 can be repeated for multiple iterative evaluations and adjustments until the therapeutic effect is achieved. During the repeated execution of step 102, the EEG response signal is updated; this EEG response signal is the EEG signal acquired after the most recent output of the electrical stimulation signal.

[0055] In this embodiment, after executing the initial output electrical stimulation signal, the signal processing system acquires the EEG response signal and calculates multi-dimensional feature change indicators, including frequency domain energy, signal complexity, and pattern recognition results, to comprehensively assess the changes in the EEG signal. Subsequently, the signal processing system makes decisions based on the combination of these multi-dimensional feature change indicators, dynamically generates the next set of stimulation parameters, and outputs a second electrical stimulation signal according to the dynamically adjusted second set of stimulation parameters via a second control command. This allows the signal processing system to operate dynamically, moving away from a preset fixed mode and improving operational flexibility.

[0056] In some embodiments, the first feature index includes: the power change of the high-frequency oscillating (HFOs) signal of the EEG response signal compared to the target EEG signal; the second feature index includes: the change of the sample entropy of the EEG response signal compared to the target EEG signal; the target EEG signal is an EEG signal that conforms to the target signal pattern; and the third decision signal includes: a first signal characterizing the continued output of the electrical stimulation signal and a second signal characterizing the cessation of the output of the electrical stimulation signal.

[0057] Based on a combination of feature change indicators in at least two dimensions and a third decision signal, a second control command is generated, including:

[0058] The evaluation score is obtained by weighted summing of the normalized values ​​of the first feature index and the normalized values ​​of the second feature index.

[0059] If the evaluation score is less than the first threshold and the third feature indicator is the first signal, a second control command is generated.

[0060] If the evaluation score is greater than the second threshold and the third characteristic indicator is the second signal, a third control command is generated; wherein the second threshold is greater than or equal to the first threshold, and the third control command is used to indicate the cessation of outputting the electrical stimulation signal.

[0061] It should be noted that after electrical stimulation, the first characteristic indicator typically decreases, while the second characteristic indicator typically increases. Therefore, in some embodiments, the first characteristic indicator includes the power decrease of the HFOs signal compared to the EEG signal during the illness, and the second characteristic indicator includes the increase of the sample entropy of the EEG response signal compared to the EEG signal during the illness.

[0062] For details regarding HFOs signals, please refer to the description in the above embodiments, which will not be repeated here.

[0063] The following example uses the implantable circular nerve stimulation system to treat epilepsy.

[0064] The power drop of the HFOs signal can indicate the therapeutic effect or the inhibitory effect on epilepsy after the most recent output electrical stimulation signal. For example, a large power drop indicates a good therapeutic effect. The power drop of the HFOs signal can be calculated in the following way:

[0065] First, baseline power calculation: After detecting the onset of EEG signals and before outputting the first electrical stimulation signal, a segment of the onset EEG signal (e.g., lasting 500 milliseconds) is extracted, then the HFOs signal components are extracted, and their average power is calculated, denoted as . .

[0066] Second, response power calculation: Within the first time window after the first electrical stimulation signal output ends, a segment of the EEG response signal is extracted, and the average power of its HFOs component is calculated using the same signal processing method, denoted as... .

[0067] Third, the calculation of the decrease value: the first characteristic indicator. This is the decrease in power, calculated using the following formula: . It is a positive value; the larger the value, the better the effect of the first electrical stimulation signal on suppressing abnormal high-frequency oscillations.

[0068] Similarly, the relevant content regarding sample entropy can be found in the description of the above embodiments, and will not be repeated here. An increase in sample entropy can indicate the therapeutic effect or the inhibitory effect on epilepsy after the most recent output electrical stimulation signal. For example, a large increase in sample entropy indicates a very good therapeutic effect.

[0069] The evaluation score is a parameter for measuring treatment effectiveness, calculated by comprehensively considering the first and second characteristic indicators. In some embodiments, different weights may be pre-assigned to the first and second characteristic indicators, and then the evaluation score may be calculated using a weighted summation method.

[0070] In this embodiment, the first feature index and the second feature index can be normalized first, and then the evaluation score can be calculated based on the normalized value.

[0071] In some embodiments, the normalized value of the first feature index The following formula is used for calculation:

[0072] Formula 1: ;in, Indicates the first characteristic index, This represents the baseline power mentioned above.

[0073] Similarly, the calculation process for the normalized value of the second characteristic indicator is similar to that for the first characteristic indicator, and will not be repeated here.

[0074] In some embodiments, the evaluation score can be calculated using the following formula two.

[0075] Formula 2: ;

[0076] in, and The preset weighting coefficients are used, and they satisfy the following conditions: . This is the normalized value of the first characteristic index. This is the normalized value of the second characteristic indicator. The weighting coefficient can be adjusted according to different epilepsy types or patient specificity. For example, for temporal lobe epilepsy where HFOs are known to be highly correlated with seizures, a weighting can be set... For patients with more significant complex changes in EEG during an attack, a more targeted approach can be adopted. .

[0077] The first and second thresholds can be set based on the clinical manifestations of epilepsy patients; no specific limits are imposed here. The conditions for generating the second control instruction are: both of the following conditions must be met simultaneously:

[0078] Condition 1: The assessment score is less than the first threshold. This indicates that, from a physiological perspective, the initial output of the electrical stimulation signal is ineffective or completely unsatisfactory.

[0079] Condition 2: The third feature indicator is the first signal. This is consistent with the judgment of the AI ​​model.

[0080] When both conditions are met, the system is convinced that the initial output electrical stimulation signal is ineffective and more aggressive intervention measures must be taken.

[0081] Conditions for generating a third control instruction: Both of the following conditions must be met simultaneously:

[0082] Condition 1: The assessment score is greater than the second threshold, where the second threshold is greater than or equal to the first threshold. This indicates that, from the two physiological dimensions of HFOs power and EEG complexity, the output electrical stimulation signal has a significant therapeutic effect and has reached a satisfactory level.

[0083] Condition 2: The third characteristic indicator is the second signal. This indicates that the data-driven AI model also judges from the overall pattern that the disease onset has been effectively suppressed.

[0084] This "double insurance" mechanism greatly reduces the risk of erroneously stopping treatment when epileptic activity is not fully suppressed, thus avoiding inadequate treatment.

[0085] In some embodiments, if the assessment score is greater than a second threshold, but the AI ​​model recommends "continue treatment," a more conservative review process can be initiated for safety reasons.

[0086] If the evaluation score falls between the first and second thresholds, a default strategy can be preset, or the classification probability of the AI ​​model can be introduced as a basis for further refined decision-making.

[0087] In this embodiment, a precise and reliable algorithm process is implemented, which effectively solves the limitations of fixed parameter stimulation and realizes individualized dynamic closed-loop control.

[0088] In some embodiments, after generating a second control command based on a combination of feature change indices in at least two dimensions and a third decision signal, the method further includes:

[0089] In response to the second control command, the stimulation parameters are cyclically adjusted to generate a dynamically changing second set of stimulation parameters, and a second electrical stimulation signal is output according to the second set of stimulation parameters until the target conditions are met and the output of the second electrical stimulation signal is stopped.

[0090] It should be noted that the process of cyclically adjusting the stimulation parameters to generate a dynamically changing second set of stimulation parameters, and outputting a second electrical stimulation signal according to the second set of stimulation parameters, can be regarded as a controlled, iterative, closed-loop process. For example, the cyclic process is as follows:

[0091] Step 1, Stimulation parameter adjustment: Adjust or update the stimulation parameters used in the most recent output electrical stimulation signal.

[0092] Step 2: Output electrical stimulation signal: Output electrical stimulation signal based on the adjusted or updated stimulation parameters.

[0093] Step 3: Determine whether to stop the loop. If yes, stop adjusting the stimulation parameters and stop outputting electrical stimulation. If no, continue the loop (continue executing steps 1, 2, and 3).

[0094] The following example uses the implantable circular nerve stimulation system to treat epilepsy.

[0095] When updating or adjusting stimulus parameters, at least one of the following methods can be used:

[0096] Intensity escalation strategy: In each cycle, the intensity of the electrical stimulation signal is increased or decreased by a fixed step size (e.g., 0.5 mA) or proportionally (e.g., by 20%).

[0097] Frequency modulation strategy: Adjust the stimulation frequency according to the physiological characteristics of the epileptic focus. For example, if the initial frequency is in the low frequency range (e.g., 10 Hz), try switching to a higher frequency (e.g., 130 Hz) to obtain a better inhibitory effect; and vice versa.

[0098] Pulse width adjustment strategy: Appropriately increasing the pulse width (e.g., from 100 μs to 200 μs) can change the energy and range of the stimulus, which may more effectively activate or inhibit specific nerve fibers.

[0099] Feedback-based targeted adjustments: Targeted adjustments are made based on the efficacy assessment parameters after the previous stimulation. For example, if the HFOs power decreases only slightly but the sample entropy improves, the focus can be on continuing to increase the intensity of electrical stimulation; if neither improves, the intensity and frequency of electrical stimulation can be adjusted simultaneously.

[0100] In some embodiments, after each output of a second electrical stimulation signal based on the adjusted stimulation parameters, the system does not immediately enter the next cycle. Instead, it repeats a simplified efficacy evaluation process. For example, after the second electrical stimulation signal is output, the system collects EEG response signals again within a time window. Then, it recalculates one or more key characteristic change indicators and performs a rapid evaluation based on the calculation results. Based on the evaluation results, it determines whether to continue outputting electrical stimulation signals and update the stimulation parameters.

[0101] In the example scenario of treating epilepsy, to ensure the safety and effectiveness of the treatment and prevent infinite loops or overstimulation, embodiments of this application set explicit loop termination conditions, i.e., target conditions. For example, the system immediately stops outputting the second electrical stimulation signal when any of the following conditions are met:

[0102] Conditions for achieving therapeutic effect: In the current cycle, based on the EEG response signal collected after the latest second electrical stimulation signal output, the system evaluation considers that the therapeutic effect has been achieved.

[0103] Maximum number of stimulations: There is an upper limit to the total number of secondary electrical stimulation signals that the system allows to output for a single epileptic seizure. Once this upper limit is reached, the output of electrical stimulation signals will be forcibly stopped regardless of the therapeutic effect to avoid nerve tissue fatigue or damage.

[0104] Maximum total stimulation energy / time: The system cumulatively calculates the total charge or total stimulation time of all electrical stimulation signals. Treatment is terminated immediately when the preset safety limit is reached.

[0105] Seizure termination criteria: An independent seizure detection algorithm determines that the patient's epileptic seizure has spontaneously terminated. At this point, continuing to output electrical stimulation signals is unnecessary, and the system immediately stops looping.

[0106] In this embodiment, by introducing the aforementioned cyclic adjustment and target condition termination mechanism, the implantable closed-loop neurostimulation system is equipped with the ability to perform multiple adaptive interventions within a single attack period, thereby achieving a balance between precise neuromodulation and risk control.

[0107] In some embodiments, the assessment score is updated each time the stimulus parameters are adjusted, and the amount of adjustment to the stimulus parameters is negatively correlated with the assessment score.

[0108] It should be noted that "negative correlation" means that the lower the current assessment score, the less ideal the result. For example, in the epilepsy treatment scenario, the lower the current assessment score, the worse the treatment effect. Correspondingly, the magnitude (adjustment amount) of the parameter adjustment will be larger; conversely, the higher the current assessment score, the more ideal the result or the treatment effect has improved well and is close to the target, so the adjustment magnitude will be smaller.

[0109] For example, when adjusting the stimulation parameters in each cycle, the system does not directly use the preset fixed step size. Instead, it first uses the evaluation score updated after the second electrical stimulation signal is output within the current cycle as a key feedback signal to dynamically calculate the parameter adjustment amount for this cycle.

[0110] In some embodiments, when the evaluation score after an output electrical stimulation signal is extremely low, it indicates that the current stimulation parameters are completely ineffective or severely insufficient. In this case, a larger adjustment step size (such as significantly increasing the stimulation intensity) can be adopted to quickly jump out of the ineffective parameter region and avoid wasting time and energy in the ineffective region.

[0111] In some embodiments, when the evaluation score after a certain output electrical stimulation signal is already at a high level (e.g., close to but not reaching the threshold for stopping treatment), it indicates that the current stimulation parameters are very close to the optimal value. At this time, automatically switching to the small step fine-tuning mode can effectively avoid overstimulation or energy waste that may be caused by excessively large step sizes.

[0112] In this embodiment of the application, this intelligent adjustment strategy can quickly jump out of the invalid parameter area, avoiding wasting time and energy in the invalid area; it can also effectively avoid overstimulation or energy waste that may be caused by excessive step size.

[0113] In some embodiments, the target condition includes at least one of the following:

[0114] The number of electrical stimulation signals output within the first duration reaches the threshold.

[0115] The total amount of charge injected within the second time period reaches the charge threshold;

[0116] The time elapsed since the first electrical stimulation signal was output has reached the duration threshold;

[0117] The first characteristic indicator has reached the decline threshold;

[0118] The second characteristic indicator has reached the rising threshold.

[0119] It should be noted that the first duration can refer to a preset time window. For example, it can be set to 60 seconds or 180 seconds. Similarly, the second duration is the same as the first duration; the second duration can be the same as or different from the first duration.

[0120] The number of stimulations threshold refers to the maximum total number of electrical stimulation signals allowed to be output within a first duration. For example, the threshold could be set to 5 times. The charge threshold refers to the safe upper limit of the total amount of charge allowed to be injected into brain tissue via electrical stimulation within a second duration.

[0121] The threshold for the number of stimuli can serve as a safety boundary to achieve safety control and prevent excessive stimulation from causing nerve tissue fatigue or potential damage. For example, in the context of epilepsy treatment, limiting the density of stimulation in a single epileptic seizure event can prevent the patient's brain from receiving excessive stimulation in a short period of time due to cyclical logic failure or persistent poor treatment efficacy.

[0122] The charge threshold condition can serve as a safety boundary to achieve safety control and avoid excessive stimulation that could lead to nerve tissue fatigue or potential damage.

[0123] The duration threshold refers to the maximum duration of the output electrical stimulation signal, starting from the first output signal. For example, the duration threshold could be set to 3 minutes. This condition can serve as a final safety condition. For instance, in epilepsy treatment, regardless of efficacy, the cycle would be forcibly terminated once the total treatment time reached this threshold. This ensures that treatment will not continue indefinitely, even in cases of abnormal counting or calculation, and also addresses epileptic seizures with abnormally prolonged durations.

[0124] In some embodiments, the first characteristic indicator can be a first characteristic indicator determined based on the target EEG response signal, wherein the target EEG response signal can refer to the EEG response signal collected after the most recent second electrical stimulation signal output, and this target EEG response signal can represent the latest neurophysiological state under the current cycle iteration. The decline threshold can refer to a target threshold set for the first characteristic indicator. For example, it may require that the HFOs power decrease by more than 60% compared to the baseline. This condition can serve as a condition for achieving therapeutic efficacy.

[0125] In some embodiments, the second characteristic indicator can be a second characteristic indicator determined based on the target EEG response signal. The target EEG response signal can refer to the EEG response signal collected after the most recent second electrical stimulation signal output, which can represent the latest neurophysiological state in the current iterative cycle. The rise threshold can refer to a threshold set for the second characteristic indicator. For example, it may require the sample entropy to rise by more than 0.3 compared to the baseline. This condition can also serve as a condition for achieving therapeutic efficacy.

[0126] In some embodiments, the target conditions are monitored using a logical "OR" relationship. That is, as soon as any one condition is met, the system immediately exits the loop and stops outputting the subsequent second electrical stimulation signal.

[0127] In some embodiments, an artifact removal time window is provided between the end time of the first electrical stimulation signal output and the first time window.

[0128] Accordingly, after generating the first control command, the method further includes:

[0129] Determine the mapping relationship between stimulus parameter intervals and time windows; wherein the average value of the stimulus parameter interval is positively correlated with the duration of the time window;

[0130] The time window corresponding to the target stimulus parameter range is selected as the artifact removal time window, wherein the target stimulus parameter range includes the first set of stimulus parameters.

[0131] It should be noted that after the implanted closed-loop neurostimulation system outputs the first electrical stimulation signal according to the first set of stimulation parameters, the EEG signal immediately following the end of the stimulation will be overwhelmed by significant stimulation artifacts. This portion of the signal cannot accurately reflect the physiological response of the neural tissue. If EEG signals are collected at this stage to calculate characteristic change indicators, the characteristic change indicators will be distorted to some extent.

[0132] To this end, this embodiment introduces and defines a key time interval—the artifact elimination time window. After the first electrical stimulation signal output ends, the implanted closed-loop neurostimulation system waits for a specific period of time until the stimulation artifacts have sufficiently decayed to a negligible level before opening the first time window to acquire pure and reliable EEG response signals.

[0133] In some embodiments, the process of determining the mapping relationship between stimulus parameter ranges and time windows may include:

[0134] An implantable closed-loop neurostimulation system pre-stores or dynamically maintains a mapping table or mapping function. This mapping table defines the correspondence between stimulation parameter ranges and time window durations. The stimulation parameter range refers to several consecutive sub-ranges formed by dividing the numerical range of one or more stimulation parameters. For example, the stimulation intensity range (e.g., 0.5mA to 10mA) can be divided into three ranges: [0.5mA, 3mA], [3mA, 6mA], and [6mA, 10mA]. Alternatively, a range within a multi-dimensional parameter space can be defined based on multiple parameters (e.g., intensity and frequency).

[0135] The duration of the time window, i.e., the specific duration of the artifact elimination time window, can be measured in milliseconds (ms). The principle for establishing the mapping relationship is that the average value of the stimulation parameter range is positively correlated with the duration of the time window. It can be understood that the stimulation parameters collectively determine the total charge injected into the tissue in a single stimulus. The higher the stimulation energy, the larger the amplitude of the generated electrical stimulation artifact, and the longer it takes for it to decay to the baseline level. Therefore, a longer artifact elimination time window needs to be allocated to a parameter range representing a higher energy level (with a larger average value).

[0136] The system selects the time window corresponding to the target stimulation parameter range as the artifact removal time window. After each output of an electrical stimulation signal, the system immediately performs this selection operation: comparing the currently used stimulation parameter with a pre-stored stimulation parameter range to determine the specific range to which the stimulation parameter belongs. The specific range containing the stimulation parameter is defined as the target stimulation parameter range. The system then queries the duration of the artifact removal time window uniquely corresponding to the target stimulation parameter range. Using this queryed duration, a timer is started at the end of the stimulation. Before this timer expires, the data acquisition channel remains closed or the acquired data is ignored. Once the timer expires, the system immediately opens the first time window and begins acquiring EEG response signals.

[0137] In this embodiment, an intelligent and personalized artifact removal strategy is achieved by establishing a dynamic mapping between stimulus parameters and artifact removal windows. For high-intensity stimuli, a sufficiently long cooling time is provided to ensure that artifacts do not affect the evaluation; for low-intensity stimuli, unnecessary excessive waiting is avoided.

[0138] In some embodiments, the step of identifying EEG signals that conform to a target signal pattern includes:

[0139] The monitored EEG signals are detected using a first detection algorithm to obtain a first detection result;

[0140] If the first detection result indicates that the signal pattern matches the target signal pattern, the monitored EEG signal is detected again using a second detection algorithm. The sensitivity of the first detection algorithm is higher than that of the second detection algorithm, and the accuracy of the second detection algorithm is higher than that of the first detection algorithm.

[0141] If the second test result indicates that the target signal pattern is met, then the EEG signal that matches the target signal pattern is identified.

[0142] It should be noted that the first detection result is obtained by real-time analysis and detection of the continuously monitored raw EEG signals through a first detection algorithm. The first detection algorithm is characterized by its high sensitivity, meaning its primary goal is to capture as many possible events or features as possible, minimizing omissions. In some embodiments, the first detection algorithm may employ rules with low computational complexity and rapid response. For example, time-domain threshold detection: continuously monitoring the amplitude or energy of the EEG signal, triggering an initial alarm when the signal amplitude exceeds a relatively low threshold for a short duration. Another example is frequency-domain energy detection: calculating power in a specific frequency band (such as the Gamma band common in epileptic seizures or lower); if the power rises rapidly and exceeds a lenient threshold, it is deemed suspicious.

[0143] The first detection result can be a binary output, indicating whether it conforms to the target signal pattern or not.

[0144] Then, after the initial screening is passed, a second detection algorithm is activated for confirmation. For example, the system will only activate the second detection algorithm to re-detect the same or most recently input EEG signal if and only if the first detection result indicates that it matches the target signal pattern.

[0145] The characteristics of the second detection algorithm: This algorithm is designed to have high accuracy (i.e., high specificity). It verifies the preliminary results of the first detection algorithm and filters out false alarms. The accuracy of the second detection algorithm is higher than that of the first detection algorithm, while its sensitivity is generally lower. In some embodiments, the second detection algorithm may employ a more complex, computationally intensive, but more discriminative model. For example, a multi-feature fusion model: simultaneously extracting multiple features of the signal, such as time domain, frequency domain, and nonlinear dynamics, to construct a more comprehensive discriminative model. Another example is a machine learning / deep learning model: using a pre-trained classifier to analyze EEG signal segments.

[0146] The second detection result is also a binary output, indicating whether it matches or does not match the target signal pattern. If the second detection result indicates that it matches the target signal pattern, then the EEG signal matching the target signal pattern has been identified. At this point, the double verification is complete.

[0147] In this embodiment, by constructing a tandem detection pipeline of "high-sensitivity screening + high-accuracy confirmation", the reliability of EEG signal detection is greatly improved without significantly increasing the average power consumption and latency of the system.

[0148] Based on the same principle as the method provided in the embodiments of this application, the embodiments of this application also provide an implantable closed-loop neurostimulation system, such as... Figure 2 As shown, it includes:

[0149] The monitoring module 201 is used to monitor EEG signals and generate a first control command when it identifies an EEG signal that matches the target signal pattern. The first control command is used to instruct the output of a first electrical stimulation signal according to a first set of stimulation parameters.

[0150] Acquisition module 202 is used to acquire the EEG response signal within the first time window after the first electrical stimulation signal is output;

[0151] The processing module 203 is used to receive the EEG response signal acquired by the acquisition module, and calculate at least two-dimensional feature change indicators based on the EEG response signal; wherein, the feature change indicators include: a first feature indicator based on the change of signal frequency domain energy in the target frequency band, a second feature indicator based on the change of signal complexity, and a third decision signal obtained by pattern recognition of the EEG response signal.

[0152] The processing module 203 is also used to generate a second control command based on a combination of feature change indicators in at least two dimensions and a third decision signal. The second control command is used to instruct the output of a second electrical stimulation signal according to a dynamically adjusted second set of stimulation parameters.

[0153] In some embodiments, the first feature index includes: the power change of the high-frequency oscillating (HFOs) signal of the EEG response signal compared to the target EEG signal; the second feature index includes: the change of the sample entropy of the EEG response signal compared to the target EEG signal; the target EEG signal is an EEG signal that conforms to the target signal pattern; and the third decision signal includes: a first signal characterizing the continued output of the electrical stimulation signal and a second signal characterizing the cessation of the output of the electrical stimulation signal.

[0154] Processing module 203 is specifically used for:

[0155] The evaluation score is obtained by weighted summing of the normalized values ​​of the first feature index and the normalized values ​​of the second feature index.

[0156] If the evaluation score is less than the first threshold and the third feature indicator is the first signal, a second control command is generated.

[0157] If the evaluation score is greater than the second threshold and the third characteristic indicator is the second signal, a third control command is generated; wherein the second threshold is greater than or equal to the first threshold, and the third control command is used to indicate the cessation of outputting the electrical stimulation signal.

[0158] In some embodiments, the implantable closed-loop neurostimulation system further includes:

[0159] The response module is used to respond to the second control command, cyclically adjust the stimulation parameters to generate a dynamically changing second set of stimulation parameters, and output a second electrical stimulation signal according to the second set of stimulation parameters until the target conditions are met and the output of the second electrical stimulation signal is stopped.

[0160] In some embodiments, the assessment score is updated each time the stimulus parameters are adjusted, and the amount of adjustment to the stimulus parameters is negatively correlated with the assessment score.

[0161] In some embodiments, the target condition includes at least one of the following:

[0162] The number of electrical stimulation signals output within the first duration reaches the threshold.

[0163] The total amount of charge injected within the second time period reaches the charge threshold;

[0164] The time elapsed since the first electrical stimulation signal was output has reached the duration threshold;

[0165] The first characteristic indicator has reached the decline threshold;

[0166] The second characteristic indicator has reached the rising threshold.

[0167] In some embodiments, an artifact removal time window is provided between the end time of the first electrical stimulation signal output and the first time window.

[0168] In some embodiments, the implantable closed-loop neurostimulation system further includes:

[0169] The mapping module is used to determine the mapping relationship between stimulus parameter intervals and time windows; wherein, the average value of the stimulus parameter interval is positively correlated with the duration of the time window;

[0170] The mapping selection module is used to select the time window corresponding to the target stimulus parameter range as the artifact elimination time window, wherein the target stimulus parameter range includes the first set of stimulus parameters.

[0171] In some embodiments, the implantable closed-loop neurostimulation system further includes: a detection module, used for:

[0172] The monitored EEG signals are detected using a first detection algorithm to obtain a first detection result;

[0173] If the first detection result indicates that the signal pattern matches the target signal pattern, the monitored EEG signal is detected again using a second detection algorithm. The sensitivity of the first detection algorithm is higher than that of the second detection algorithm, and the accuracy of the second detection algorithm is higher than that of the first detection algorithm.

[0174] If the second test result indicates that the target signal pattern is met, then the EEG signal that matches the target signal pattern is identified.

[0175] The implantable closed-loop neurostimulation system provided in this application embodiment can achieve… Figure 1 The various processes implemented in the method embodiments are not described in detail here to avoid repetition.

[0176] The implantable closed-loop neurostimulation system provided in this application, after executing the initial output electrical stimulation signal, acquires the electroencephalogram (EEG) response signal and calculates multi-dimensional feature change indicators, including frequency domain energy, signal complexity, and pattern recognition results, to comprehensively assess the changes in the EEG signal. Subsequently, the signal processing system makes decisions based on the combination of these multi-dimensional feature change indicators, dynamically generates the next set of stimulation parameters, and outputs a second electrical stimulation signal according to the dynamically adjusted second set of stimulation parameters via a second control command. This allows the signal processing system to operate dynamically, departing from a preset fixed mode, thus improving operational flexibility.

[0177] The implantable closed-loop neurostimulation system of this application embodiment can execute the signal processing method provided in the embodiment of this application. 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 signal processing 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 signal processing methods shown above. They will not be repeated here.

[0178] 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.

[0179] In an alternative embodiment, a signal processing system, such as Figure 3 As shown, Figure 3 The signal processing system 3000 shown includes a processor 3001 and a memory 3003. The processor 3001 and the memory 3003 are connected, for example, via a bus 3002. Optionally, the signal processing system 3000 may further include a transceiver 3004, which can be used for data interaction between the signal processing system and other electronic devices, such as data transmission and / or data reception. It should be noted that in practical applications, the transceiver 3004 is not limited to one type, and the structure of this signal processing system 3000 does not constitute a limitation on the embodiments of this application.

[0180] Processor 3001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 3001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0181] Bus 3002 may include a pathway for transmitting information between the aforementioned components. Bus 3002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 3002 can 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.

[0182] The memory 3003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), 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, other magnetic storage devices, or any other medium capable of carrying or storing computer programs and capable of being read by a computer, without limitation herein.

[0183] The memory 3003 is used to store computer programs that execute the embodiments of this application, and the execution is controlled by the processor 3001. The processor 3001 is used to execute the computer programs stored in the memory 3003 to implement the steps shown in the foregoing method embodiments.

[0184] 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.

[0185] 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.

[0186] The terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the illustrations or text descriptions.

[0187] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0188] The above description is only an optional implementation method for some implementation scenarios 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: A monitoring module is used to monitor electroencephalogram (EEG) signals and generate a first control command when an EEG signal that conforms to a target signal pattern is detected. The first control command is used to instruct the output of a first electrical stimulation signal according to a first set of stimulation parameters. The EEG signal that conforms to the target signal pattern is an EEG signal indicating an illness. The acquisition module is used to acquire the EEG response signal within the first time window after the first electrical stimulation signal is output; The processing module is used to receive the EEG response signal acquired by the acquisition module, and calculate feature change indicators in at least two dimensions based on the EEG response signal; wherein the feature change indicators include: a first feature indicator based on the change of signal frequency domain energy in the target frequency band, a second feature indicator based on the change of signal complexity, and a third decision signal based on the pattern recognition obtained from the EEG response signal; The processing module is further configured to generate a second control instruction based on a combination of the feature change index in at least two dimensions and the third decision signal, wherein the second control instruction is configured to instruct the output of a second electrical stimulation signal according to a dynamically adjusted second set of stimulation parameters; The response module is used to respond to the second control command by cyclically adjusting the stimulation parameters to generate a dynamically changing second set of stimulation parameters, and outputting a second electrical stimulation signal according to the second set of stimulation parameters until the target conditions are met and the output of the second electrical stimulation signal is stopped.

2. The implantable closed-loop neurostimulation system according to claim 1, characterized in that, The first feature index includes: the power change of the high-frequency oscillating (HFOs) signal of the EEG response signal compared to the target EEG signal; the second feature index includes: the change of the sample entropy of the EEG response signal compared to the target EEG signal; the target EEG signal is the EEG signal that conforms to the target signal pattern; and the third decision signal includes: a first signal representing the continuation of outputting the electrical stimulation signal and a second signal representing the cessation of outputting the electrical stimulation signal. The processing module is specifically used for: The evaluation score is obtained by weighted summing of the normalized values ​​of the first feature index and the normalized values ​​of the second feature index. If the evaluation score is less than the first threshold and the third decision signal is the first signal, the second control command is generated. If the evaluation score is greater than the second threshold and the third decision signal is the second signal, a third control command is generated; wherein the second threshold is greater than or equal to the first threshold, and the third control command is used to instruct the output of the electrical stimulation signal to be stopped.

3. The implantable closed-loop neurostimulation system according to claim 2, characterized in that, During each adjustment of the stimulus parameters, the assessment score is updated, and the adjustment amount of the stimulus parameters is negatively correlated with the assessment score.

4. The implantable closed-loop neurostimulation system according to claim 2, characterized in that, The target condition includes at least one of the following: The number of electrical stimulation signals output within the first duration reaches the threshold. The total amount of charge injected within the second time period reaches the charge threshold; The duration of the first electrical stimulation signal output reaches the duration threshold; The first characteristic indicator reaches the decline threshold; The second characteristic indicator has reached the rising threshold.

5. The implantable closed-loop neurostimulation system according to claim 1, characterized in that, There is an artifact removal time window between the end time of the first electrical stimulation signal output and the first time window.

6. The implantable closed-loop neurostimulation system according to claim 5, characterized in that, The implantable closed-loop neurostimulation system also includes: The mapping module is used to determine the mapping relationship between stimulus parameter intervals and time windows; wherein the average value of the stimulus parameter interval is positively correlated with the duration of the time window; The mapping selection module is used to select the time window corresponding to the target stimulus parameter range as the artifact elimination time window, wherein the target stimulus parameter range includes the first set of stimulus parameters.

7. The implantable closed-loop neurostimulation system according to claim 1, characterized in that, The implantable closed-loop neurostimulation system further includes: a detection module, used for: The monitored EEG signals are detected using a first detection algorithm to obtain a first detection result; If the first detection result indicates that the signal conforms to the target signal pattern, the monitored EEG signal is detected again using a second detection algorithm to obtain a second detection result; the sensitivity of the first detection algorithm is higher than that of the second detection algorithm, and the accuracy of the second detection algorithm is higher than that of the first detection algorithm. If the second detection result indicates that the target signal pattern is met, the EEG signal that meets the target signal pattern is identified.

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.

Images