WO2022166685A1 - 用于植入式电刺激装置的自响应检测参数优化方法及系统 - Google Patents

用于植入式电刺激装置的自响应检测参数优化方法及系统 Download PDF

Info

Publication number
WO2022166685A1
WO2022166685A1 PCT/CN2022/073771 CN2022073771W WO2022166685A1 WO 2022166685 A1 WO2022166685 A1 WO 2022166685A1 CN 2022073771 W CN2022073771 W CN 2022073771W WO 2022166685 A1 WO2022166685 A1 WO 2022166685A1
Authority
WO
WIPO (PCT)
Prior art keywords
detection
sampling
eeg
value
current
Prior art date
Application number
PCT/CN2022/073771
Other languages
English (en)
French (fr)
Inventor
曹鹏
林婷
吴承瀚
陈新蕾
Original Assignee
杭州诺为医疗技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 杭州诺为医疗技术有限公司 filed Critical 杭州诺为医疗技术有限公司
Priority to EP22748964.8A priority Critical patent/EP4289359A1/en
Publication of WO2022166685A1 publication Critical patent/WO2022166685A1/zh

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36064—Epilepsy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36067—Movement disorders, e.g. tremor or Parkinson disease
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36125—Details of circuitry or electric components
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36132—Control systems using patient feedback
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36135—Control systems using physiological parameters
    • A61N1/36139—Control systems using physiological parameters with automatic adjustment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/362—Heart stimulators
    • A61N1/365—Heart stimulators controlled by a physiological parameter, e.g. heart potential
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/37—Intracranial electroencephalography [IC-EEG], e.g. electrocorticography [ECoG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/375—Electroencephalography [EEG] using biofeedback
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/388—Nerve conduction study, e.g. detecting action potential of peripheral nerves

Definitions

  • This document relates to the technical field of medical equipment, in particular to a method and system for optimizing self-response detection parameters for implantable electrical stimulation devices.
  • implantable medical systems have been widely used in clinical medicine, including implantable electrical stimulation systems, implantable drug injection systems, and the like.
  • the implantable electrical stimulation system includes an implantable nerve electrical stimulation system and an implantable cardiac electrical stimulation system.
  • the implantable electrical nerve stimulation system mainly includes an implanted electrical pulse generator, a stimulation electrode and an external controller.
  • the electrical stimulation pulses generated by the implantable electrical pulse generator are transmitted to the stimulation electrodes, and the stimulation electrodes are transmitted to specific neural targets for electrical stimulation, so that conditions such as Parkinson's and epilepsy can be treated.
  • the implantable electrical pulse generator not only conducts electrical stimulation on the nerve target to treat the disease, but also collects the sampling signal through the acquisition circuit to further judge the treatment effect of the electrical stimulation system on the disease according to the sampling signal, so as to adjust the implantable type.
  • the electrical stimulation pulses sent by the electrical pulse generator to the stimulation electrodes it is not possible to predict and intervene in advance to prevent the occurrence of events such as Parkinson's and epilepsy. How to predict the symptoms and intervene in advance based on the predicted results has become a technical problem that needs to be solved urgently.
  • the purpose of one or more embodiments of this specification is to provide a self-response detection parameter optimization method and system for an implantable electrical stimulation device, which can detect and predict diseases in real time, so that early intervention can be performed based on the prediction results, Prevent epilepsy and other events from happening.
  • a self-response detection parameter optimization method for an implantable electrical stimulation device including: collecting a sampling signal based on current detection parameters; judging that the sampling signal exceeds a preset detection threshold; obtaining a self-response The optimal detection parameter; the current detection parameter is adjusted based on the optimal detection parameter.
  • an implantable electrical stimulation device including: a collection module, which collects a sampling signal based on current detection parameters; a judgment module, which judges that the sampling signal exceeds a preset detection threshold; an acquisition module, which obtains the maximum value from the response an optimal detection parameter; an adjustment module, which adjusts the current detection parameter based on the optimal detection parameter.
  • an implantable electrical stimulation system comprising an external device and an implantable electrical stimulation device as described above, the external device being communicatively connected to the implantable electrical stimulation device.
  • a storage medium for computer-readable storage, the storage medium stores one or more programs, and when the one or more programs can be executed by one or more processors, the above-mentioned implementation is achieved The steps of the self-response detection parameter optimization method described in this paper.
  • the self-response detection parameter optimization method for an implantable electrical stimulation device provided by the present application first collects a sampling signal based on the current detection parameters, and then determines the sampling signal. After the preset detection threshold is exceeded, the optimal detection parameters are obtained.
  • the detection threshold here is the range value of the EEG signal when it is judged whether it is epilepsy or the epilepsy or an imminent seizure based on the collected sampling signal such as the EEG signal. If the current detection parameter is set too small, it may trigger the response treatment under normal circumstances, resulting in too high sensitivity of the response treatment. If the sensitivity is too high, it will also increase the risk of side effects.
  • the optimal detection parameters are obtained from the response, and the current detection parameters are adjusted based on the obtained optimal detection parameters, that is, the current detection parameters are adjusted to the optimal detection parameters.
  • the current detection parameters are further optimized, so that epilepsy and other events can be accurately detected in the process of real-time detection of patients, which can reduce the false detection rate and trigger response treatment to achieve the purpose of precise treatment, so as to timely intervene in treatment to prevent The occurrence of epilepsy and other events.
  • FIG. 1 is a schematic diagram of steps of a method for optimizing self-response detection parameters for an implantable electrical stimulation device according to an embodiment of the present specification.
  • FIG. 2 is a schematic diagram of steps of another method for optimizing self-response detection parameters of an implantable electrical stimulation device according to an embodiment of the present specification.
  • FIG. 3 is a schematic diagram of steps of another method for optimizing self-response detection parameters of an implantable electrical stimulation device according to an embodiment of the present specification.
  • FIG. 4 is a schematic diagram of steps of yet another method for optimizing self-response detection parameters of an implantable electrical stimulation device provided by an embodiment of the present specification.
  • FIG. 5 is a schematic diagram of steps of yet another method for optimizing self-response detection parameters of an implantable electrical stimulation device provided by an embodiment of the present specification.
  • FIG. 6 is a schematic diagram of steps of another method for optimizing self-response detection parameters of an implantable electrical stimulation device according to an embodiment of the present specification.
  • FIG. 7 is a schematic diagram of steps of yet another method for optimizing self-response detection parameters of an implantable electrical stimulation device according to an embodiment of the present specification.
  • FIG. 8 is a schematic structural diagram of an implantable electrical stimulation device provided by an embodiment of the present specification.
  • FIG. 9 is a schematic structural diagram of another implantable electrical stimulation device provided by an embodiment of the present specification.
  • the self-response detection parameter optimization method for an implantable electrical stimulation device provided by the present application can detect and predict diseases in real time, so that early intervention can be performed based on the prediction results to prevent the occurrence of epilepsy and other events.
  • the method for optimizing the self-response detection parameters of the implantable electrical stimulation device and each step thereof provided by the present application will be described in detail below.
  • the implantable electrical stimulation device to which the self-response detection parameter optimization method for the implantable electrical stimulation device provided by the present application is applicable may be implanted in the human body, contact with the brain tissue through a sampling circuit, and collect the EEG of the human brain tissue. After analyzing and judging the information displayed by the sampling signal, the stimulation circuit is adjusted, and the stimulation circuit adjusts the electrical pulses sent to the brain tissue.
  • sampling signal mentioned in this application document may be a physiological signal collected on a human body or an animal.
  • a method for optimizing self-response detection parameters of an implantable electrical stimulation device provided in the embodiment of this specification is suitable for an implantable electrical stimulation system in clinical medicine, and can be used for epilepsy and other patients.
  • Real-time detection, and accurate detection of epilepsy and other events during the treatment process so that early intervention and treatment can be performed to avoid the occurrence of epilepsy and other events.
  • the self-response detection parameter optimization method for an implantable electrical stimulation device continuously obtains optimal detection parameters in the real-time detection process, so that more accurate sampling signals can be collected based on the optimal detection parameters.
  • the self-response detection parameter optimization method for an implantable electrical stimulation device provided by the embodiments of this specification includes:
  • Step 10 collecting sampling signals based on the current detection parameters
  • the first thing that the implantable electrical stimulation device needs to do is to collect sampling signals, such as the EEG signal in the patient's skull, to obtain the EEG sampling value. signal etc.
  • the current detection parameters can be initial settings after the medical staff makes a preliminary judgment based on the patient's condition.
  • the current detection parameters generally include detection algorithms, detection thresholds, detection intervals, etc.
  • the current detection parameters can also be selected from the factory default values of the detection equipment.
  • Step 20 determine that the sampling signal exceeds the preset detection threshold
  • the sampling signal is processed, and the sampling signal is compared with a preset detection threshold, so as to determine whether to optimize the current detection parameter.
  • the setting principle of the preset detection threshold can be set according to the detection accuracy and sensitivity of epilepsy and other events.
  • the preset detection threshold may be set by medical staff, for example, according to the parameters included in the sampling signal, and when the sampling signal includes multiple parameters, the corresponding preset detection threshold may include multiple parameters. , when the sampling signal exceeds one of the preset detection thresholds, it is determined that the sampling signal exceeds the preset detection threshold.
  • the preset detection threshold may be 50% of the sampled signal when an event such as epilepsy occurs.
  • Step 30 Obtain optimal detection parameters from the response
  • the optimal detection parameter is obtained from the response, and the purpose is to replace the current detection parameter with the optimal detection parameter.
  • the optimal detection parameters may be detection parameters that have been set in advance according to clinical experience, or may be detection parameters obtained in other ways, which are not limited herein.
  • Step 40 Adjust the current detection parameters based on the optimal detection parameters.
  • the microprocessor of the implantable electrical stimulation device can send parameter adjustment instructions and optimal detection parameters to the sampling circuit, and the sampling circuit performs detection parameter adjustment after receiving the parameter adjustment instructions. command, replace the current detection parameter with the optimal detection parameter and collect the sampling signal as the next detection parameter.
  • step 30 obtaining optimal detection parameters from self-response, specifically including:
  • Step 300 Calculate the current event detection rate from the response
  • the optimal detection parameters obtained from the self-response in step 30 may be obtained by the self-response detection parameter optimization method provided in the embodiments of this specification. After judging that the sampling signal exceeds the preset detection threshold, it is determined that an event such as epilepsy is detected, and then the current event detection rate is calculated from the response.
  • the current event detection rate is the detection rate of epilepsy and other events so far, and the detection rate can be calculated by dividing the number of detected epilepsy and other events from the start of detection to the present divided by the total number of detections.
  • detection can be performed at intervals of a set time period, so the total number of detections in the set time period can be known, and the number of detected epilepsy and other events can also be known, so the current event detection rate can be calculated from the response. .
  • Step 310 determine that the current event detection rate is greater than the preset target detection rate
  • the preset target detection rate can be set according to actual clinical experience, for example, the preset target detection rate within one week is 80%. Determine whether the current event detection rate is greater than the preset target detection rate. After judging that the current event detection rate is greater than the preset target detection rate, it can be inferred that the current detection parameters of epilepsy and other events are not accurate and sensitive enough, and the current detection parameters need to be further optimized. , for more timely and accurate detection of events such as epilepsy.
  • Step 320 Calculate optimal detection parameters from the response.
  • the implantable electrical stimulation device After the optimal detection parameters are obtained from the self-response, the implantable electrical stimulation device sends the optimal detection parameters and parameter adjustment instructions to the acquisition circuit, and the acquisition circuit adjusts the original current detection parameters based on the optimal detection parameters.
  • step 10 before collecting sampling signals based on current detection parameters, the method for optimizing self-response detection parameters for implantable electrical stimulation devices provided by the embodiments of this specification further includes:
  • Step 50 Set the sampling analysis result as a detection flag.
  • the sampling analysis result can be set as the detection flag.
  • the sampling analysis result here can be compared with the recent sampling analysis result of the patient as the current historical data, such as the sampling collected during the last sampling window.
  • the sampling analysis results obtained after analyzing the recent sampling analysis results according to the sampling analysis results, determine the symptoms or physiological characteristics of the patient when epilepsy and other events occur, which can be quantified as waveform characteristics, and then set the detection flag for the sampling analysis results.
  • the sampling analysis results include half-wave characteristics such as half-wave amplitude and half-wave duration.
  • Corresponding detection is then performed according to the detection flag. For example, different current detection parameters are used to collect sampling signals, and corresponding detection flags can be set for different recent sampling analysis results obtained by different patients at different times to perform corresponding detection, which improves the personalization and pertinence of real-time detection of patients.
  • step 50 setting the sampling analysis result as a detection flag, specifically including:
  • Step 500 Identify the waveform characteristics of the sampled signal
  • the waveform feature of the sampled signal can be identified by changing the sampling amplitude of the sampled signal, such as identifying the half-wave feature of the sampled signal, so that the epileptic wave and the normal brain wave can be identified by the half-wave feature.
  • the sampled signal is the latest sampled analysis result, which may be the sampled signal during the previous sampling window.
  • Step 510 Calculate the window parameters of the sampled signal during the sampling window
  • the sampling window period is the period of real-time detection of the patient. Calculate the window parameters of the sampling signal during the last sampling window, such as the change in sampling amplitude and the change in frequency, and the line length function and area function during the last sampling window can be calculated. . The window parameters of the sampled signal during the previous sampling window can be used for detection flags during the next sampling window.
  • Step 520 take the waveform feature and the window parameter as the sampling analysis result, and determine whether the sampling analysis result conforms to the event occurrence characteristic;
  • the current detection parameters can be automatically adjusted. Before that, the collected electrical signal is characterized by identification. Due to the individual differences and even the complexity of the waveform characteristics themselves, it will be identified.
  • the different features of the device can be combined in different ways, such as the combination of line length and area, the combination of line length and bandpass, etc., which can detect more accurately and identify more diverse features to improve the accuracy of real-time detection and prediction, and improve epilepsy.
  • the detection rate of EEG signals at the onset of other events can prevent the occurrence of epilepsy events.
  • the waveform characteristics such as half-wave characteristics and window parameters as the recent sampling analysis results
  • Step 530 If yes, set the sampling analysis result as the detection flag.
  • the sampling analysis result is set as the detection flag, and then the corresponding detection instruction is opened, so that the corresponding detection parameter can be used to collect the sampling signal.
  • Step 10 Collect sampling signals based on the current detection parameters, including:
  • Step 100 Based on different detection flags, use corresponding current detection parameters to collect sampling signals.
  • the detection marks of each patient in different periods are different, and the sampling signals are collected using the corresponding current detection parameters.
  • Different patients have different detection signs, and different current detection parameters are used to collect sampling signals, so that individualized and targeted real-time detection of patients can be realized.
  • step 500 Identify the waveform characteristics of the sampled signal, which specifically includes:
  • Step 501 Identify the half-wave digital signal of the sampled signal.
  • the half-wave digital signal includes a start point and an end point, wherein the start point is the initial EEG sample value of the EEG sample value, and the end point is the end point of the EEG sample value EEG sampling value;
  • the waveform feature identifying the sampling signal may be a half-wave digital signal identifying the sampling signal.
  • the half-wave digital signal includes a starting point and an ending point.
  • the starting point of the sampling signal during the sampling window in the sampling signal is equal to the initial brain during the sampling window.
  • Electrical sampling value, the end point is the end point EEG sampling value within the sampling window period.
  • Step 502 Calculate the half-wave duration of the half-wave digital signal, where the half-wave duration is the difference between the time point corresponding to the end point and the time point corresponding to the start point.
  • the half-wave duration of the half-wave digital signal is calculated, and the half-wave duration is equal to the difference between the time point corresponding to the end point and the time point corresponding to the start point.
  • step 501 identifying the half-wave digital signal of the sampled signal, the half-wave digital signal
  • the signal includes a start point and an end point, wherein the start point is the initial EEG sample value of the EEG sample value, and the end point is the end point EEG sample value of the EEG sample value, specifically including:
  • Step 503 Calculate the end threshold based on the current EEG sampling value and the preset hysteresis value
  • the current EEG sampling value here is the same as the EEG sampling value in the current sampling window period.
  • the hysteresis value is preset, which refers to the hysteresis value of the sampling value.
  • the preset hysteresis value is a fixed value and will not change according to the difference of each sampling window period.
  • the end threshold is used to compare with the next EEG sampling value to determine the end EEG sampling value of the current EEG sampling value.
  • the end threshold is calculated based on the EEG sampled values during the current sampling window and the preset hysteresis value.
  • the end threshold is the initial EEG sampling value of the current EEG sampling value plus and minus the hysteresis value to obtain a range value, and then the current EEG sampling value within the current sampling window is measured in turn, and the current EEG sampling value is compared with the end. threshold for comparison. If the current EEG sampling value is less than the end threshold, recalculate the end threshold, measure the current EEG sampling value, and compare the current EEG sampling value with the end threshold until the current EEG sampling value exceeds the end threshold, then determine The current EEG sample value appears at the end point.
  • Step 504 measure the next EEG sampling value
  • the next EEG sample is measured with the aim of comparing the next EEG sample with the end threshold obtained in the previous step.
  • Step 505 Compare the next EEG sampling value with the end threshold
  • the initial EEG sample value of the next EEG sample value is equal to the current EEG sample value Endpoint EEG sampling value.
  • Step 506 If the next EEG sampling value exceeds the end threshold, calculate the amplitude of the half-wave digital signal, and the amplitude is the end EEG sampling value of the current EEG sampling value minus the initial EEG sampling value of the current EEG sampling value ; otherwise, return to continue to execute step 504: continue to measure the next EEG sample value.
  • the self-response detection parameter optimization method for implantable electrical stimulation devices first collects sampling signals based on the current detection parameters, and after judging that the sampling signals exceed the preset detection threshold, obtains the self-response
  • the optimal detection parameter, the detection threshold is the range value of the EEG signal when it is judged whether it is epilepsy or other symptoms or the EEG signal when the seizure is about to occur based on the collected sampling signal such as the EEG signal. If the current detection parameter is set too small, it may trigger the response treatment under normal circumstances, resulting in too high sensitivity of the response treatment. If the sensitivity is too high, it will also increase the risk of side effects.
  • the optimal detection parameters are obtained from the response, and the current detection parameters are adjusted based on the obtained optimal detection parameters, that is, the current detection parameters are adjusted to the optimal detection parameters.
  • the current detection parameters are further optimized, so that epilepsy and other events can be accurately detected in the process of real-time detection of patients, which can reduce the false detection rate and trigger response treatment to achieve the purpose of precise treatment, so as to timely intervene in treatment to prevent The occurrence of epilepsy and other events.
  • an implantable electrical stimulation device 1 is provided in the embodiment of the present specification.
  • the implantable electrical stimulation device is suitable for an implantable electrical stimulation system in clinical medicine, and can perform real-time detection on epilepsy and other patients. , and accurately detect the occurrence of epilepsy and other events during the treatment process, so that early intervention and treatment can be performed to avoid the occurrence of epilepsy and other events.
  • the implantable electrical stimulation device continuously obtains optimal detection parameters in the real-time detection process, so that more accurate sampling signals can be collected based on the optimal detection parameters.
  • the implantable electrical stimulation device includes:
  • the acquisition module 10 collects sampling signals based on the current detection parameters
  • the first thing that the implantable electrical stimulation device needs to do is to collect sampling signals, such as the EEG signal in the patient's skull, to obtain the EEG sampling value. signal etc.
  • the current detection parameters can be initial settings after medical staff cannot make a judgment based on the patient's condition.
  • the current detection parameters generally include detection algorithms, detection thresholds, detection intervals, etc.
  • the current detection parameters can also be selected from the factory default values of the detection equipment.
  • the judgment module 20 judges that the sampling signal exceeds the preset detection threshold
  • the sampling signal is processed, and the sampling signal is compared with a preset detection threshold, so as to determine whether to optimize the current detection parameter.
  • the setting principle of the preset detection threshold can be set according to the detection accuracy and sensitivity of epilepsy and other events.
  • the preset detection threshold may be set by medical staff, for example, according to the parameters included in the sampling signal, and when the sampling signal includes multiple parameters, the corresponding preset detection threshold may include multiple parameters.
  • the preset detection threshold may be 50% of the sampled signal when an event such as epilepsy occurs.
  • the obtaining module 30 obtains the optimal detection parameter from the response
  • the optimal detection parameter is obtained by self-response, and this step does not need to be started manually.
  • the purpose is to replace the current detection parameter with the optimal detection parameter.
  • the optimal detection parameters may be detection parameters that have been set in advance according to clinical experience, or may be detection parameters obtained in other ways, which are not limited herein.
  • the adjustment module 40 adjusts the current detection parameters based on the optimal detection parameters.
  • the microprocessor of the implantable electrical stimulation device can send parameter adjustment instructions and optimal detection parameters to the sampling circuit, and the sampling circuit performs detection parameter adjustment after receiving the parameter adjustment instructions. command, replace the current detection parameter with the optimal detection parameter and collect the sampling signal as the next detection parameter.
  • the acquisition module 30 is specifically used for:
  • Step 300 Calculate the current event detection rate from the response
  • the optimal detection parameters obtained from the self-response in step 30 may be obtained by the self-response detection parameter optimization method provided in the embodiments of this specification. After judging that the sampling signal exceeds the preset detection threshold, it is determined that an event such as epilepsy is detected, and then the current event detection rate is calculated from the response.
  • the current event detection rate is the detection rate of epilepsy and other events so far, and the detection rate can be calculated by dividing the number of detected epilepsy and other events from the start of detection to the present divided by the total number of detections. In the process of real-time detection, detection can be performed at intervals of set time periods, so the total number of detections can be known, and the number of detected epilepsy and other events can also be known, so the current event detection rate can be calculated.
  • Step 310 determine that the current event detection rate is greater than the preset target detection rate
  • the preset target detection rate can be set according to actual clinical experience, for example, the preset target detection rate within one week is 80%. Determine whether the current event detection rate is greater than the preset target detection rate. After judging that the current event detection rate is greater than the preset target detection rate, it can be inferred that the current detection parameters of epilepsy and other events are not accurate and sensitive enough, and the current detection parameters need to be further optimized. , for more timely and accurate detection of events such as epilepsy.
  • Step 320 Calculate optimal detection parameters from the response.
  • the implantable electrical stimulation device After obtaining the optimal detection parameters, the implantable electrical stimulation device sends the optimal detection parameters and parameter adjustment instructions to the acquisition circuit, and the acquisition circuit adjusts the original current detection parameters based on the optimal detection parameters.
  • the implantable electrical stimulation device provided by the embodiments of the present specification further includes a sampling analysis module 50, and the sampling analysis module 50 is used for:
  • Step 50 Set the sampling analysis result as a detection flag.
  • the sampling analysis result Before each start of the detection cycle and start to collect the sampling signal, the sampling analysis result can be set as the detection flag.
  • the sampling analysis result here can be compared with the recent sampling analysis result of the patient as the current historical data.
  • the obtained sampling analysis result according to the sampling analysis result, determines the symptoms or physiological characteristics of the patient when epilepsy and other events occur, which can be quantified as waveform characteristics, and then the sampling analysis result is set as a detection flag.
  • the sampling analysis results include half-wave characteristics such as half-wave amplitude and half-wave duration.
  • Corresponding detection is then performed according to the detection flag. For example, different current detection parameters are used to collect sampling signals, and corresponding detection flags can be set for different recent sampling analysis results obtained by different patients at different times to perform corresponding detection, which improves the personalization and pertinence of real-time detection of patients.
  • the sampling analysis module 50 is specifically used for:
  • Step 500 Identify the waveform characteristics of the sampled signal
  • the waveform feature of the sampled signal can be identified by changing the sampling amplitude of the sampled signal, such as identifying the half-wave feature of the sampled signal, so that the epileptic wave and the normal brain wave can be identified by the half-wave feature.
  • the sampled signal is the latest sampled analysis result, which may be the sampled signal during the previous sampling window.
  • Step 510 Calculate the window parameters of the sampled signal during the sampling window
  • the sampling window period is the period of real-time detection of the patient. Calculate the window parameters of the sampling signal during the last sampling window, such as the change in sampling amplitude and the change in frequency, and the line length function and area function during the last sampling window can be calculated. . The window parameters of the sampled signal during the previous sampling window can be used for detection flags during the next sampling window.
  • Step 520 take the waveform feature and the window parameter as the sampling analysis result, and determine whether the sampling analysis result conforms to the event occurrence characteristic;
  • the current detection parameters can be automatically adjusted. Before that, the collected electrical signal is characterized by identification. Due to the individual differences and even the complexity of the waveform characteristics themselves, it will be identified.
  • the different features of the device can be combined in different ways, such as the combination of line length and area, the combination of line length and bandpass, etc., which can detect more accurately and identify more diverse features to improve the accuracy of real-time detection and prediction, and improve epilepsy.
  • the detection rate of EEG signals at the onset of other events can prevent the occurrence of epilepsy events.
  • the waveform characteristics such as half-wave characteristics and window parameters as the recent sampling analysis results
  • Step 530 If yes, set the sampling analysis result as the detection flag.
  • the sampling analysis result is set as the detection flag, and then the corresponding detection instruction is opened, so that the corresponding detection parameter can be used to collect the sampling signal.
  • the implantable electrical stimulation device provided by the embodiments of this specification, the sampling analysis module 50, is also specifically used for:
  • Step 501 Identify the half-wave digital signal of the sampled signal.
  • the half-wave digital signal includes a start point and an end point, wherein the start point is the initial EEG sample value of the EEG sample value, and the end point is the end point of the EEG sample value EEG sampling value;
  • the waveform characteristic of identifying the sampling signal can be a half-wave digital signal identifying the sampling signal.
  • the half-wave digital signal includes a starting point and an ending point.
  • the sampling signal is a sampling signal within a sampling window period, and the corresponding starting point is equal to the recent sampling window period.
  • the initial EEG sample value of the inner EEG sample value, and the end point is the end point sample value of the EEG sample value during the sampling window.
  • Step 502 Calculate the half-wave duration of the half-wave digital signal, where the half-wave duration is the difference between the time point corresponding to the end point and the time point corresponding to the start point.
  • the half-wave duration of the half-wave digital signal is calculated, and the half-wave duration is equal to the difference between the time point corresponding to the end point and the time point corresponding to the start point.
  • the implantable electrical stimulation device provided by the embodiments of this specification, the sampling analysis module 50, is also specifically used for:
  • Step 503 Calculate the end threshold based on the current EEG sampling value and the preset hysteresis value
  • the current EEG sampling value here is the same as described above, and is the EEG sampling value during the current sampling window.
  • the preset hysteresis value is preset, the hysteresis value refers to the hysteresis value of the sampling value, and the preset hysteresis value is a fixed value, which will not change according to the difference of each sampling window period.
  • the end threshold is used to compare with the next EEG sampling value to determine the end EEG sampling value of the current EEG sampling value.
  • the end threshold is calculated based on the EEG sampled values during the recent sampling window and the preset hysteresis value.
  • the end threshold is obtained by adding and subtracting the hysteresis value from the initial EEG sampling value of the EEG sampling value, which is a range value. Then measure the current EEG sampling value during the current sampling window in turn, and compare the current EEG sampling value with the end threshold; if the current EEG sampling value is less than the end threshold, recalculate the end threshold, measure the current EEG Figure sampling value, compare the current EEG sampling value and the end threshold, until the current EEG sampling value exceeds the end threshold, then the current EEG sampling value of the manual appears the end point.
  • Step 504 measure the next EEG sampling value
  • the next EEG sample is measured with the aim of comparing the next EEG sample with the end threshold obtained in the previous step.
  • Step 505 Compare the next EEG sampling value with the end threshold
  • the initial EEG sample value of the next EEG sample value is equal to the current EEG sample value Endpoint EEG sampling value.
  • Step 506 If the next EEG sampling value exceeds the end threshold, calculate the amplitude of the half-wave digital signal, and the amplitude is the end EEG sampling value of the current EEG sampling value minus the initial EEG sampling value of the current EEG sampling value. sampling value; otherwise, return to continue to execute step 504: continue to measure the next EEG sampling value.
  • the self-response detection parameter optimization method for implantable electrical stimulation devices first collects sampling signals based on the current detection parameters, and after judging that the sampling signals exceed the preset detection threshold, obtains the self-response
  • the optimal detection parameter, the detection threshold is the range value of the EEG signal when it is judged whether it is epilepsy or other symptoms or the EEG signal when the seizure is about to occur based on the collected sampling signal such as the EEG signal. If the current detection parameter is set too small, it may trigger the response treatment under normal circumstances, resulting in too high sensitivity of the response treatment. If the sensitivity is too high, it will also increase the risk of side effects.
  • the optimal detection parameter is obtained from the response, and the current detection parameter is adjusted based on the obtained optimal detection parameter, that is, the current detection parameter is adjusted to the optimal detection parameter.
  • the current detection parameters are further optimized, so that epilepsy and other events can be accurately detected in the process of real-time detection of patients, which can reduce the false detection rate and trigger response treatment to achieve the purpose of precise treatment, so as to timely intervene in treatment to prevent The occurrence of epilepsy and other events.
  • An implantable electrical stimulation system provided by an embodiment of the present specification includes an external device and an implantable electrical stimulation device as shown in FIGS. 8-9 , and the external device is communicatively connected to the implantable electrical stimulation device.
  • the implantable electrical stimulation device is suitable for implantable electrical stimulation systems in clinical medicine. It can detect epilepsy and other patients in real time, and accurately predict the occurrence of epilepsy and other events during the treatment process, so that the treatment can be intervened early to avoid the occurrence of epilepsy. The occurrence of epilepsy and other events.
  • the implantable electrical stimulation device continuously obtains optimal detection parameters in the real-time detection process, so that more accurate sampling signals can be collected based on the optimal detection parameters.
  • the implantable electrical stimulation device includes:
  • the acquisition module 10 collects sampling signals based on the current detection parameters
  • the first thing that the implantable electrical stimulation device needs to do is to collect sampling signals, such as the EEG signal in the patient's skull, to obtain the EEG sampling value. signal etc.
  • the current detection parameters can be initial settings after medical staff cannot make a judgment based on the patient's condition.
  • the current detection parameters generally include detection algorithms, detection thresholds, detection intervals, etc.
  • the current detection parameters can also be selected from the factory default values of the detection equipment.
  • the judgment module 20 judges that the sampling signal exceeds the preset detection threshold
  • the sampling signal is processed, and the sampling signal is compared with a preset detection threshold, so as to determine whether to optimize the current detection parameter.
  • the setting principle of the preset detection threshold can be set according to the detection accuracy and sensitivity of epilepsy and other events.
  • the preset detection threshold may be set by medical staff, for example, according to the parameters included in the sampling signal, and when the sampling signal includes multiple parameters, the corresponding preset detection threshold may include multiple parameters.
  • the preset detection threshold may be 50% of the sampled signal when an event such as epilepsy occurs.
  • the obtaining module 30 obtains the optimal detection parameter from the response
  • the optimal detection parameter is obtained by self-response, and the purpose is to replace the current detection parameter with the optimal detection parameter.
  • the optimal detection parameters may be detection parameters that have been set in advance according to clinical experience, or may be detection parameters obtained in other ways, which are not limited herein.
  • the adjustment module 40 adjusts the current detection parameters based on the optimal detection parameters.
  • the microprocessor of the implantable electrical stimulation device can send parameter adjustment instructions and optimal detection parameters to the sampling circuit, and the sampling circuit performs detection parameter adjustment after receiving the parameter adjustment instructions. command, replace the current detection parameter with the optimal detection parameter and collect the sampling signal as the next detection parameter.
  • the self-response detection parameter optimization method for implantable electrical stimulation devices first collects sampling signals based on the current detection parameters, and after judging that the sampling signals exceed the preset detection threshold, obtains the self-response
  • the optimal detection parameter, the detection threshold is the range value of the EEG signal when it is judged whether it is epilepsy or other symptoms or the EEG signal when the seizure is about to occur based on the collected sampling signal such as the EEG signal. If the current detection parameter is set too small, it may trigger the response treatment under normal circumstances, resulting in too high sensitivity of the response treatment. If the sensitivity is too high, it will also increase the risk of side effects.
  • the optimal detection parameters are obtained from the response, and the current detection parameters are adjusted based on the obtained optimal detection parameters, that is, the current detection parameters are adjusted to the optimal detection parameters.
  • the current detection parameters are further optimized, so that epilepsy and other events can be accurately detected in the process of real-time detection of patients, which can reduce the false detection rate and trigger response treatment to achieve the purpose of precise treatment, so as to timely intervene in treatment to prevent The occurrence of epilepsy and other events.
  • a storage medium provided by an embodiment of the present specification is used for computer-readable storage, where the storage medium stores one or more programs, and when the one or more programs can be executed by one or more processors, implements the following:
  • the steps of the self-response detection parameter optimization method shown in FIG. 1 to FIG. 7 specifically include:
  • Step 10 collecting sampling signals based on the current detection parameters
  • the first thing that the implantable electrical stimulation device needs to do is to collect sampling signals, such as the EEG signal in the patient's skull, to obtain the EEG sampling value. signal etc.
  • the current detection parameters can be initial settings after the medical staff makes a preliminary judgment based on the patient's condition.
  • the current detection parameters generally include detection algorithms, detection thresholds, detection intervals, etc.
  • the current detection parameters can also be selected from the factory default values of the detection equipment. Step 20: determine that the sampling signal exceeds the preset detection threshold;
  • the sampling signal is processed, and the sampling signal is compared with a preset detection threshold, so as to determine whether to optimize the current detection parameter.
  • the setting principle of the preset detection threshold can be set according to the detection accuracy and sensitivity of epilepsy and other events.
  • the preset detection threshold may be set by medical staff, for example, according to the parameters included in the sampling signal, and when the sampling signal includes multiple parameters, the corresponding preset detection threshold may include multiple parameters. , when the sampling signal exceeds one of the preset detection thresholds, it is determined that the sampling signal exceeds the preset detection threshold.
  • the preset detection threshold may be 50% of the sampled signal when an event such as epilepsy occurs.
  • Step 30 Obtain optimal detection parameters from the response
  • the optimal detection parameter is obtained by self-response, and the purpose is to replace the current detection parameter with the optimal detection parameter.
  • the optimal detection parameters may be detection parameters that have been set in advance according to clinical experience, or may be detection parameters obtained in other ways, which are not limited herein.
  • Step 40 Adjust the current detection parameters based on the optimal detection parameters.
  • the microprocessor of the implantable electrical stimulation device can send parameter adjustment instructions and optimal detection parameters to the sampling circuit, and the sampling circuit performs detection parameter adjustment after receiving the parameter adjustment instructions. command, replace the current detection parameter with the optimal detection parameter and collect the sampling signal as the next detection parameter.
  • the self-response detection parameter optimization method for implantable electrical stimulation devices first collects sampling signals based on the current detection parameters, and after judging that the sampling signals exceed the preset detection threshold, obtains the self-response
  • the optimal detection parameter, the detection threshold is the range value of the EEG signal when it is judged whether it is epilepsy or other symptoms or the EEG signal when the seizure is about to occur based on the collected sampling signal such as the EEG signal. If the current detection parameter is set too small, it may trigger the response treatment under normal circumstances, resulting in too high sensitivity of the response treatment. If the sensitivity is too high, it will also increase the risk of side effects.
  • the optimal detection parameters are obtained from the response, and the current detection parameters are adjusted based on the obtained optimal detection parameters, that is, the current detection parameters are adjusted to the optimal detection parameters.
  • the current detection parameters are further optimized, so that epilepsy and other events can be accurately detected in the process of real-time detection of patients, which can reduce the false detection rate and trigger response treatment to achieve the purpose of precise treatment, so as to timely intervene in treatment to prevent The occurrence of epilepsy and other events.
  • a typical implementation device is a computer.
  • the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or A combination of any of these devices.
  • Computer-readable storage media includes both persistent and non-permanent, removable and non-removable media, and storage of information can be implemented by any method or technology.
  • Information may be computer readable instructions, data structures, modules of programs, or other data.
  • Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
  • computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

Abstract

本说明书一个或多个实施例公开了一种用于植入式电刺激装置的自响应检测参数优化方法及系统。该用于植入式电刺激装置的自响应检测参数优化方法，包括：基于当前检测参数采集生理信号；判断出所述采样信号超出预设的检测阈值；自响应获取最优检测参数；基于所述最优检测参数调整所述当前检测参数，可以对病症进行实时检测和预测，从而可以基于预测结果进行提前干预，防止癫痫等事件的发生。

Description

用于植入式电刺激装置的自响应检测参数优化方法及系统 技术领域
本文件涉及医疗设备技术领域，尤其涉及一种用于植入式电刺激装置的自响应检测参数优化方法及系统。
背景技术
目前，植入式医疗系统已经广泛应用于医学临床上，包括植入式电刺激系统、植入式药物注射系统等。其中植入式电刺激系统包括植入式神经电刺激系统和植入式心脏电刺激系统，植入式神经电刺激系统主要包括植入体内的植入式电脉冲发生器、刺激电极以及体外的控制器。植入式电脉冲发生器所产生的电刺激脉冲传输到刺激电极，由刺激电极传输至特定神经靶点进行电刺激，从而可以治疗如帕金森、癫痫等病症。
通常，植入式电脉冲发生器在对神经靶点进行电刺激用来治疗病症的同时，还通过采集电路采集采样信号以进一步根据采样信号判断电刺激系统对病症的治疗效果从而调整植入式电脉冲发生器向刺激电极发送的电刺激脉冲。但是，并不能对病症进行预测和提前干预，防止比如帕金森、癫痫等事件的发生。如何对病症进行预测，以此基于预测结果进行提前干预，成为亟需解决的技术问题。
发明内容
本说明书一个或多个实施例的目的是提供一种用于植入式电刺激装置的自响应检测参数优化方法及系统，可以对病症进行实时检测和预测，从而可以基于预测结果进行提前干预，防止癫痫等事件的发生。
为解决上述技术问题，本说明书一个或多个实施例是这样实现的：
第一方面，提出了一种用于植入式电刺激装置的自响应检测参数优化方法，包括：基于当前检测参数采集采样信号；判断出所述采样信号超出预设的检测阈值；自响应获取最优检测参数；基于所述最优检测参数调整所述当前检测参数。
第二方面，提出了一种植入式电刺激装置，包括：采集模块，基于当前检测参数采集采样信号；判断模块，判断出所述采样信号超出预设的检测阈值；获取模块，自响应获取最优检测参数；调整模块，基于所述最优检测参数调整所述当前检测参数。
第三方面，提出了一种植入式电刺激系统，所述系统包括外部设备和如上文所述的植入式电刺激装置，所述外部设备与所述植入式电刺激装置通信连接。
第四方面，提出了一种存储介质，用于计算机可读存储，所述存储介质存储有一个或者多个程序，所述一个或者多个程序可被一个或者多个处理器执行时，实现如上文所述的自响应检测参数优化方法的步骤。
由以上本说明书一个或多个实施例提供的技术方案可见，本申请提供的用于植入式电刺激装置的自响应检测参数优化方法，首先基于当前检测参数采集采样信号，在判断出采样信号超出预设的检测阈值后，获取最优检测参数，这里的检测阈值是基于采集的采样信号比如脑电信号判断是否为癫痫等病症发作或者即将发作时的脑电信号的范围值。如果当前检测参数设置的过小，在正常情况下有可能触发响应治疗，导致响应治疗的灵敏度过高，灵敏度过高也会增加副作用风险，因此在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，并且基于获取到的最优检测参数调整当前检测参数，即将当前检测参数调整为最优检测参数。从而对当前检测参数做进一步优化，从而在对患者实时检测的过程中可以对癫痫等事件进行精准的检测，降低误检率的同时，触发响应治疗，达到精准治疗的目的，从而及时干预治疗防止癫痫等事件的发生。
附图说明
为了更清楚地说明本说明书一个或多个实施例或现有技术中的技术方案，下面将对一个或多个实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本说明书中记载的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本说明书实施例提供的一种用于植入式电刺激装置的自响应检测参数优化方法的步骤示意图。
图2是本说明书实施例提供的另一种用于植入式电刺激装置的自响应检测参数优化方法的步骤示意图。
图3是本说明书实施例提供的又一种用于植入式电刺激装置的自响应检测参数优化方法的步骤示意图。
图4是本说明书实施例提供的又一种用于植入式电刺激装置的自响应检测参数优化方法的步骤示意图。
图5是本说明书实施例提供的又一种用于植入式电刺激装置的自响应检测参数优化方法的步骤示意图。
图6是本说明书实施例提供的又一种用于植入式电刺激装置的自响应检测参数优化方法的步骤示意图。
图7是本说明书实施例提供的又一种用于植入式电刺激装置的自响应检测参数优化方法的步骤示意图。
图8是本说明书实施例提供的一种植入式电刺激装置的结构示意图。
图9是本说明书实施例提供的另一种植入式电刺激装置的结构示意图。
具体实施方式
为了使本技术领域的人员更好地理解本说明书中的技术方案，下面将结合本说明书一个或多个实施例中的附图，对本说明书一个或多个实施例中的技术方案进行清楚、完整地描述，显然，所描述的一个或多个实施例仅仅是本说明书一部分实施例，而不是全部的实施例。基于本说明书中的一个或多个实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都应当属于本文件的保护范围。
本申请提供的用于植入式电刺激装置的自响应检测参数优化方法可以对病症进行实时检测和预测，从而可以基于预测结果进行提前干预，防止癫痫等事件的发生。下面将详细地描述本申请提供的用于植入式电刺激装置的自响应检测参数优化方法及其各个步骤。
本申请提供的用于植入式电刺激装置的自响应检测参数优化方法所适用的植入式电刺激装置可以为植入人体体内，通过采样电路与脑组织接触，采集人体脑组织的脑电信号，通过对采样信号所显示的信息进行分析判断后调整刺激电路，刺激电路调整向脑组织发送的电脉冲。
需要说明的是，本申请文件中提到的采样信号可以是在人或动物等身上采集的生理信号。
实施例一
参照图1所示，为本说明书实施例提供的一种用于植入式电刺激装置的自响应检测参数优化方法，适用于临床医学上的植入式电刺激系统，可以对癫痫等患者进行实时检测，并且在治疗过程中对发生癫痫等事件进行精准检测，从而可以及早干预治疗，避免癫痫等事件的发生。该用于植入式电刺激装置的自响应检测参数优化方法，在实时检测过程中不断获取最优的检测参数，从而可以基于该最优检测参数采集更准确的采样信号。本说明书实施例提供的用于植入式电刺激装置的自响应检测参数优化方法，包括：
步骤10：基于当前检测参数采集采样信号；
植入式电刺激装置首先需要做的是采集采样信号比如患者颅内的脑电信号，得到脑电图采样值，采样信号根据植入式电刺激装置的不同还可以是心电信号、神经电信号等。当前检测参数可以是医务工作人员根据患者病情进行初步判断后初始设定，当前检测参数一般包括检测算法、检测阈值、检测区间等，当前检测参数也可以选用检测设备的出厂默认值。
步骤20：判断出采样信号超出预设的检测阈值；
在获取到采样信号后对采样信号进行处理，将采样信号与预设的检测阈值进行比较，以此判断是否对当前检测参数进行优化。预设的检测阈值的设置原则可以根据癫痫等事件的检测精准度和灵敏度进行设定。预设的检测阈值可以是医务工作人员进行设定，比如根据采样信号所包含的参数进行相应的设定，采样信号所包含的参数为多个时，对应的预设的检测阈值可以包含多个，当采样信号超出其中一个预设的检测阈值时即判断出采样信号超出预设的检测阈值。比如预设的检测阈值可以是癫痫等事件发生时的采样信号的50％。
步骤30：自响应获取最优检测参数；
在判断出采样信号超出预设的检测阈值后自响应获取最优检测参数，目的是采用最优检测参数替代当前检测参数。
最优检测参数可以是事先已经根据临床经验设定的检测参数，也可以是其它方式获取的检测参数，在此不做限定。
步骤40：基于最优检测参数调整当前检测参数。
基于获取的最优检测参数调整当前检测参数，植入式电刺激装置的微处理器可以发送参数调节指令和最优检测参数发送给采样电路，采样电路在接收到参数调节指令后执行检测参数调节指令，将最优检测参数替换掉当前检测参数后成为下一个检测参数采集采样信号。
参照图2所示，在一些实施例中，本说明书实施例提供的用于植入式电刺激装置的自响应检测参数优化方法中，步骤30：自响应获取最优检测参数，具体包括：
步骤300：自响应计算当前事件检测率；
在步骤30中的自响应获取最优检测参数，可以通过本说明书实施例提供的自响应检测参数优化方法得到。在判断出采样信号超出预设的检测阈值后确定检出癫痫等事件，接下来自响应计算当前事件检测率。当前事件检测率是截止目前癫痫等事件的检测率，检测率的计算可以是从检测开始截止目前检出癫痫等事件的次数除以检测总次数。实时检测的过程中可以间隔设定时间段进行检测，因此在该设定时间段内检测总次数可以知晓，检出癫痫等事件的次数也是可以知晓的，因此可以自响应计算得到当前事件检测率。
步骤310：判断出当前事件检测率大于预设的目标检测率；
预设的目标检测率可以根据实际临床经验进行设定，比如一星期内预设的目标检测率为80％。判断当前事件检测率是否大于预设的目标检测率，在判断出当前事件检测率大于预设的目标检测率后可以推断出癫痫等事件的当前检测参数不够精准和灵敏，需要进一步优化当前检测参数，以便更及时准确地检测出癫痫等事件。
步骤320：自响应计算最优检测参数。
自响应计算最优检测参数。在自响应得到最优检测参数后，植入式电刺激装置将最优检测参数和参数调节指令发送至采集电路，采集电路基于最优检测参数调节原有的当前检测参数。
参照图3所示，在一些实施例中，步骤10：基于当前检测参数采集采样信号之前，本说明书实施例提供的用于植入式电刺激装置的自响应检测参数优化方法，还包括：
步骤50：将采样分析结果设置为检测标志。
在每次启动检测周期开始采集采样信号之前，可以将采样分析结果设置为检测标志，这里的采样分析结果可以是患者较之当前作为历史资料的近期采样分析结果比如上一采样窗口期间采集的采样结果，在对近期采样分析结果进行分析后得到的采样分析结果，根据该采样分析结果确定患者出现癫痫等事件时候的症状或者生理特征，可以量化为波形特征，进而将该采样分析结果设置检测标志。采样分析结果包括半波振幅、半波持续时间等半波特征。后续根据检测标志进行对应的检测。比如采用不同的当前检测参数采集采样信号，对于不同患者在不同时间得到的近期采样分析结果不同可以设置对应的检测标志从而进行相应的检测，提高了对患者实时检测的个性化和针对性。
参照图4所示，在一些实施例中，本说明书实施例提供的用于植入式电刺激装置的自响应检测参数优化方法中，步骤50：将采样分析结果设置为检测标志，具体包括：
步骤500：识别采样信号的波形特征；
可以通过对采样信号的采样幅度变化来识别采样信号的波形特征，比如识别采样信号的半波特征，从而可以半波特征来识别癫痫波与正常脑电波。采样信号是近期采样分析结果，可以是上一采样窗口期间的采样信号。
识别采样信号的波形特征，如果波形特征符合该患者癫痫事件发生的症状或者生理特征，则标记并存储该波形特征，用于接下来采样窗口期间的检测标志。
步骤510：计算采样窗口期间采样信号的窗口参数；
采样窗口期间是对患者进行实时检测的这段时间，计算上一采样窗口期间采样信号的窗口参数比如采样振幅的变化、频率的变化，可以计算该上一采样窗口期间的线长函数和面积函数。上一采样窗口期间采样信号的窗口参数可以用于接下来采样窗口期间的检测标志。
步骤520：将波形特征和窗口参数作为采样分析结果，并且判断采样分析结果是否符合事件发生特征；
将采集的采样信号与预设的检测阈值进行比较，可对当前检测参数进行自动调节，在此 之前，对采集的电信号进行特征识别，由于波形特征本身存在个体差异甚至复杂性，将识别出的不同特征进行不同方式的组合比如线长与面积的组合，线长与带通的组合等，可以更精准的检出和更多样的特征识别来提高实时检测与预测的准确性，提高癫痫等事件发作时脑电信号的检测率，阻止癫痫事件的发生。
在分别得到作为近期采样分析结果的波形特征比如半波特征，以及窗口参数后，进一步根据波形特征和窗口参数确定是否符合事件发生的症状和生理特征，如果符合则将该采样分析结果设置为检测标志。在设置了检测标志后根据检测标志执行相应的检测操作。
步骤530：如果是，则将采样分析结果设置为检测标志。
判断作为采样分析结果的近期采样窗口期间采样信号的波形特征和窗口参数是否为患者癫痫发作的生理特征。若是，则将采样分析结果设置为检测标志，进而开启对应的检测指令，从而可以采用对应的检测参数采集采样信号。
参照图5所示，在一些实施例中，本说明书实施例提供的用于植入式电刺激装置的自响应检测参数优化方法，在完成步骤50：将分析结果设置为检测标志的条件下，步骤10：基于当前检测参数采集采样信号，具体包括：
步骤100：基于不同的检测标志，采用对应的当前检测参数采集采样信号。
每一位患者在不同时期的检测标志不同，采用对应的当前检测参数采集采样信号。不同的患者的检测标志不同，采用不同的当前检测参数采集采样信号，由此可以实现对患者个性化和针对性的实时检测。
参照图6所示，在一些实施例中，本说明书实施例提供的用于植入式电刺激装置的自响应检测参数优化方法，步骤500：识别采样信号的波形特征，具体包括：
步骤501：识别采样信号的半波数字信号，半波数字信号包括起始点和结束点，其中，起始点为脑电图采样值的初始脑电采样值，结束点为脑电图采样值的终点脑电采样值；
识别采样信号的波形特征可以是识别采样信号的半波数字信号，半波数字信号包括起始点和结束点，采样信号中采样窗口期间内的采样信号的起始点等于该采样窗口期间内的初始脑电采样值，结束点为该采样窗口期间内的终点脑电采样值。
步骤502：计算半波数字信号的半波持续时间，半波持续时间为结束点对应时间点与起始点对应时间点的差值。
计算半波数字信号的半波持续时间，该半波持续时间等于结束点对应时间点与起始点对应时间点的差值。
参照图7所示，在一些实施例中，本说明书实施例提供的用于植入式电刺激装置的自响 应检测参数优化方法中，步骤501：识别采样信号的半波数字信号，半波数字信号包括起始点和结束点，其中，起始点为脑电图采样值的初始脑电采样值，结束点为脑电图采样值的终点脑电采样值，具体包括：
步骤503：基于当前脑电图采样值和预设的磁滞值计算结束阈值；
此处的当前脑电图采样值同上文所述为当前采样窗口期间内的脑电图采样值。磁滞值是预设的，是指采样值的磁滞值，预设的磁滞值是一个固定值，不会根据每一次采样窗口期的不同而发生变化。结束阈值用于与下一个脑电图采样值进行比较，确定当前脑电图采样值的终点脑电采样值。
基于当前采样窗口期间内的脑电图采样值和预设的磁滞值计算结束阈值。
结束阈值为当前脑电图采样值的初始脑电采样值加减磁滞值得到一个范围值，然后依次测量当前采样窗口期间内的当前脑电图采样值，将当前脑电图采样值与结束阈值进行比较。如果当前脑电图采样值小于结束阈值，则重新计算结束阈值、测量当前脑电图采样值并且比较当前脑电图采样值与结束阈值，直到当前脑电图采样值超过结束阈值，则判断出当前脑电图采样值出现结束点。
步骤504：测量下一个脑电图采样值；
测量下一个脑电图采样值，目的是将该下一个脑电图采样值与上一步骤中得到的结束阈值进行比较。
步骤505：将下一个脑电图采样值与结束阈值进行比较；
在将下一个脑电图采样值与结束阈值进行比较时，如果下一个脑电图采样值超出结束阈值，则下一个脑电图采样值的初始脑电采样值为当前脑电图采样值的终点脑电采样值。
步骤506：如果下一个脑电图采样值超出结束阈值，则计算半波数字信号的振幅，振幅为当前脑电采样值的终点脑电采样值减去当前脑电采样值的初始脑电采样值；否则，返回继续执行步骤504：继续测量下一个脑电图采样值。
通过上述技术方案，本申请提供的用于植入式电刺激装置的自响应检测参数优化方法，首先基于当前检测参数采集采样信号，在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，这里的检测阈值是基于采集的采样信号比如脑电信号判断是否为癫痫等病症发作或者即将发作时的脑电信号的范围值。如果当前检测参数设置的过小，在正常情况下有可能触发响应治疗，导致响应治疗的灵敏度过高，灵敏度过高也会增加副作用风险，因此在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，并且基于获取到的最优检测参数调整当前检测参数，即将当前检测参数调整为最优检测参数。从而对当前检测参 数做进一步优化，从而在对患者实时检测的过程中可以对癫痫等事件进行精准的检测，降低误检率的同时，触发响应治疗，达到精准治疗的目的，从而及时干预治疗防止癫痫等事件的发生。
实施例二
参照图8所示，为本说明书实施例提供的一种植入式电刺激装置1，该植入式电刺激装置适用于临床医学上的植入式电刺激系统，可以对癫痫等患者进行实时检测，并且在治疗过程中对发生癫痫等事件进行精准检测，从而可以及早干预治疗，避免癫痫等事件的发生。该用于植入式电刺激装置，在实时检测过程中不断获取最优的检测参数，从而可以基于该最优检测参数采集更准确的采样信号。该植入式电刺激装置，包括：
采集模块10，基于当前检测参数采集采样信号；
植入式电刺激装置首先需要做的是采集采样信号比如患者颅内的脑电信号，得到脑电图采样值，采样信号根据植入式电刺激装置的不同还可以是心电信号、神经电信号等。当前检测参数可以是医务工作人员根据患者病情进行不出判断后初始设定，当前检测参数一般包括检测算法、检测阈值、检测区间等，当前检测参数也可以选用检测设备的出厂默认值。
判断模块20，判断出采样信号超出预设的检测阈值；
在获取到采样信号后对采样信号进行处理，将采样信号与预设的检测阈值进行比较，以此判断是否对当前检测参数进行优化。预设的检测阈值的设置原则可以根据癫痫等事件的检测精准度和灵敏度进行设定。预设的检测阈值可以是医务工作人员进行设定，比如根据采样信号所包含的参数进行相应的设定，采样信号所包含的参数为多个时，对应的预设的检测阈值可以包含多个当采样信号超出其中一个预设的检测阈值时即判断出采样信号超出预设的检测阈值。比如预设的检测阈值可以是癫痫等事件发生时的采样信号的50％。
获取模块30，自响应获取最优检测参数；
在判断出采样信号超出预设的检测阈值后自响应获取最优检测参数，不需要人为启动这个步骤，目的是采用最优检测参数替代当前检测参数。
最优检测参数可以是事先已经根据临床经验设定的检测参数，也可以是其它方式获取的检测参数，在此不做限定。
调整模块40，基于最优检测参数调整当前检测参数。
基于获取的最优检测参数调整当前检测参数，植入式电刺激装置的微处理器可以发送参数调节指令和最优检测参数发送给采样电路，采样电路在接收到参数调节指令后执行检测参数调节指令，将最优检测参数替换掉当前检测参数后成为下一个检测参数采集采样信号。
在一些实施例中，本说明书实施例提供的植入式电刺激装置中，获取模块30，具体用于：
步骤300：自响应计算当前事件检测率；
在步骤30中的自响应获取最优检测参数，可以通过本说明书实施例提供的自响应检测参数优化方法得到。在判断出采样信号超出预设的检测阈值后确定检出癫痫等事件，接下来自响应计算当前事件检测率。当前事件检测率是截止目前癫痫等事件的检测率，检测率的计算可以是从检测开始截止目前检出癫痫等事件的次数除以检测总次数。实时检测的过程中可以间隔设定时间段进行检测，因此检测总次数可以知晓，检出癫痫等事件的次数也是可以知晓的，因此可以计算得到当前事件检测率。
步骤310：判断出当前事件检测率大于预设的目标检测率；
预设的目标检测率可以根据实际临床经验进行设定，比如一星期内预设的目标检测率为80％。判断当前事件检测率是否大于预设的目标检测率，在判断出当前事件检测率大于预设的目标检测率后可以推断出癫痫等事件的当前检测参数不够精准和灵敏，需要进一步优化当前检测参数，以便更及时准确地检测出癫痫等事件。
步骤320：自响应计算最优检测参数。
自响应计算最优检测参数。在得到最优检测参数后，植入式电刺激装置将最优检测参数和参数调节指令发送至采集电路，采集电路基于最优检测参数调节原有的当前检测参数。
参照图9所示，在一些实施例中，本说明书实施例提供的植入式电刺激装置，还包括采样分析模块50，采样分析模块50，用于：
步骤50：将采样分析结果设置为检测标志。
在每次启动检测周期开始采集采样信号之前，可以将采样分析结果设置为检测标志，这里的采样分析结果可以是患者较之当前作为历史资料的近期采样分析结果，在对近期采样分析结果进行分析后得到的采样分析结果，根据该采样分析结果确定患者出现癫痫等事件时候的症状或者生理特征，可以量化为波形特征，进而将该采样分析结果设置检测标志。采样分析结果包括半波振幅、半波持续时间等半波特征。后续根据检测标志进行对应的检测。比如采用不同的当前检测参数采集采样信号，对于不同患者在不同时间得到的近期采样分析结果不同可以设置对应的检测标志从而进行相应的检测，提高了对患者实时检测的个性化和针对性。
在一些实施例中，本说明书实施例提供的植入式电刺激装置，采样分析模块50，具体用于：
步骤500：识别采样信号的波形特征；
可以通过对采样信号的采样幅度变化来识别采样信号的波形特征，比如识别采样信号的半波特征，从而可以半波特征来识别癫痫波与正常脑电波。采样信号是近期采样分析结果，可以是上一采样窗口期间的采样信号。
识别采样信号的波形特征，如果波形特征符合该患者癫痫事件发生的症状或者生理特征，则标记并存储该波形特征，用于接下来采样窗口期间的检测标志。
步骤510：计算采样窗口期间采样信号的窗口参数；
采样窗口期间是对患者进行实时检测的这段时间，计算上一采样窗口期间采样信号的窗口参数比如采样振幅的变化、频率的变化，可以计算该上一采样窗口期间的线长函数和面积函数。上一采样窗口期间采样信号的窗口参数可以用于接下来采样窗口期间的检测标志。
步骤520：将波形特征和窗口参数作为采样分析结果，并且判断采样分析结果是否符合事件发生特征；
将采集的采样信号与预设的检测阈值进行比较，可对当前检测参数进行自动调节，在此之前，对采集的电信号进行特征识别，由于波形特征本身存在个体差异甚至复杂性，将识别出的不同特征进行不同方式的组合比如线长与面积的组合，线长与带通的组合等，可以更精准的检出和更多样的特征识别来提高实时检测与预测的准确性，提高癫痫等事件发作时脑电信号的检测率，阻止癫痫事件的发生。
在分别得到作为近期采样分析结果的波形特征比如半波特征，以及窗口参数后，进一步根据波形特征和窗口参数确定是否符合事件发生的症状和生理特征，如果符合则将该采样分析结果设置为检测标志。在设置了检测标志后根据检测标志执行相应的检测操作。
步骤530：如果是，则将采样分析结果设置为检测标志。
判断作为采样分析结果的近期采样窗口期间采样信号的波形特征和窗口参数是否为患者癫痫发作的生理特征。若是，则将采样分析结果设置为检测标志，进而开启对应的检测指令，从而可以采用对应的检测参数采集采样信号。
在一些实施例中，本说明书实施例提供的植入式电刺激装置，采样分析模块50，还具体用于：
步骤501：识别采样信号的半波数字信号，半波数字信号包括起始点和结束点，其中，起始点为脑电图采样值的初始脑电采样值，结束点为脑电图采样值的终点脑电采样值；
识别采样信号的波形特征可以是识别采样信号的半波数字信号，半波数字信号包括起始点和结束点，采样信号为一个采样窗口期间内的采样信号，对应的起始点等于该近期采样窗口期间内脑电图采样值的初始脑电图样本值，结束点为该采样窗口期间的脑电图采样值的终 点采样值。
步骤502：计算半波数字信号的半波持续时间，半波持续时间为结束点对应时间点与起始点对应时间点的差值。
计算半波数字信号的半波持续时间，该半波持续时间等于结束点对应时间点与起始点对应时间点的差值。
在一些实施例中，本说明书实施例提供的植入式电刺激装置，采样分析模块50，还具体用于：
步骤503：基于当前脑电图采样值和预设的磁滞值计算结束阈值；
此处的当前脑电图采样值同上文所述，为当前采样窗口期间内的脑电图采样值。预设的磁滞值是预设的，磁滞值是指采样值的磁滞值，预设的磁滞值是一个固定值，不会根据每一次采样窗口期的不同而发生变化。结束阈值用于与下一个脑电图采样值进行比较，确定当前脑电图采样值的终点脑电采样值。
基于近期采样窗口期间内的脑电图采样值和预设的磁滞值计算结束阈值。结束阈值为脑电图采样值的初始脑电采样值加减磁滞值得到，是一个范围值。然后依次测量当前采样窗口期间内的当前脑电图采样值，将当前脑电图采样值与结束阈值进行比较；如果当前脑电图采样值小于结束阈值，则重新计算结束阈值、测量当前脑电图采样值、比较当前脑电图采样值与结束阈值，直到当前脑电图采样值超过结束阈值，则说明书当前脑电图采集值出现结束点。
步骤504：测量下一个脑电图采样值；
测量下一个脑电图采样值，目的是将该下一个脑电图采样值与上一步骤中得到的结束阈值进行比较。
步骤505：将下一个脑电图采样值与结束阈值进行比较；
在将下一个脑电图采样值与结束阈值进行比较时，如果下一个脑电图采样值超出结束阈值，则下一个脑电图采样值的初始脑电采样值为当前脑电图采样值的终点脑电采样值。
步骤506：如果下一个脑电图采样值超出结束阈值，则计算半波数字信号的振幅，振幅为当前脑电图采样值的终点脑电采样值减去当前脑电图采样值的初始脑电采样值；否则，返回继续执行步骤504：继续测量下一个脑电图采样值。
通过上述技术方案，本申请提供的用于植入式电刺激装置的自响应检测参数优化方法，首先基于当前检测参数采集采样信号，在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，这里的检测阈值是基于采集的采样信号比如脑电信号判断是否为癫痫等病症发作或者即将发作时的脑电信号的范围值。如果当前检测参数设置的过小，在正常情况下 有可能触发响应治疗，导致响应治疗的灵敏度过高，灵敏度过高也会增加副作用风险，因此在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，并且基于获取到的最优检测参数调整当前检测参数，即将当前检测参数调整为最优检测参数。从而对当前检测参数做进一步优化，从而在对患者实时检测的过程中可以对癫痫等事件进行精准的检测，降低误检率的同时，触发响应治疗，达到精准治疗的目的，从而及时干预治疗防止癫痫等事件的发生。
实施例三
本说明书实施例提供的一种植入式电刺激系统，包括外部设备和如图8-图9所示的植入式电刺激装置，外部设备与植入式电刺激装置通信连接。该植入式电刺激装置适用于临床医学上的植入式电刺激系统，可以对癫痫等患者进行实时检测，并且在治疗过程中对发生癫痫等事件进行精准预测，从而可以及早干预治疗，避免癫痫等事件的发生。该用于植入式电刺激装置，在实时检测过程中不断获取最优的检测参数，从而可以基于该最优检测参数采集更准确的采样信号。该植入式电刺激装置，包括：
采集模块10，基于当前检测参数采集采样信号；
植入式电刺激装置首先需要做的是采集采样信号比如患者颅内的脑电信号，得到脑电图采样值，采样信号根据植入式电刺激装置的不同还可以是心电信号、神经电信号等。当前检测参数可以是医务工作人员根据患者病情进行不出判断后初始设定，当前检测参数一般包括检测算法、检测阈值、检测区间等，当前检测参数也可以选用检测设备的出厂默认值。
判断模块20，判断出采样信号超出预设的检测阈值；
在获取到采样信号后对采样信号进行处理，将采样信号与预设的检测阈值进行比较，以此判断是否对当前检测参数进行优化。预设的检测阈值的设置原则可以根据癫痫等事件的检测精准度和灵敏度进行设定。预设的检测阈值可以是医务工作人员进行设定，比如根据采样信号所包含的参数进行相应的设定，采样信号所包含的参数为多个时，对应的预设的检测阈值可以包含多个当采样信号超出其中一个预设的检测阈值时即判断出采样信号超出预设的检测阈值。比如预设的检测阈值可以是癫痫等事件发生时的采样信号的50％。
获取模块30，自响应获取最优检测参数；
在判断出采样信号超出预设的检测阈值后自响应获取最优检测参数，目的是采用最优检测参数替代当前检测参数。
最优检测参数可以是事先已经根据临床经验设定的检测参数，也可以是其它方式获取的检测参数，在此不做限定。
调整模块40，基于最优检测参数调整当前检测参数。
基于获取的最优检测参数调整当前检测参数，植入式电刺激装置的微处理器可以发送参数调节指令和最优检测参数发送给采样电路，采样电路在接收到参数调节指令后执行检测参数调节指令，将最优检测参数替换掉当前检测参数后成为下一个检测参数采集采样信号。
通过上述技术方案，本申请提供的用于植入式电刺激装置的自响应检测参数优化方法，首先基于当前检测参数采集采样信号，在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，这里的检测阈值是基于采集的采样信号比如脑电信号判断是否为癫痫等病症发作或者即将发作时的脑电信号的范围值。如果当前检测参数设置的过小，在正常情况下有可能触发响应治疗，导致响应治疗的灵敏度过高，灵敏度过高也会增加副作用风险，因此在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，并且基于获取到的最优检测参数调整当前检测参数，即将当前检测参数调整为最优检测参数。从而对当前检测参数做进一步优化，从而在对患者实时检测的过程中可以对癫痫等事件进行精准的检测，降低误检率的同时，触发响应治疗，达到精准治疗的目的，从而及时干预治疗防止癫痫等事件的发生。
实施例四
本说明书实施例提供的一种存储介质，用于计算机可读存储，所述存储介质存储有一个或者多个程序，所述一个或者多个程序可被一个或者多个处理器执行时，实现如图1至图7所示的自响应检测参数优化方法的步骤，具体包括：
步骤10：基于当前检测参数采集采样信号；
植入式电刺激装置首先需要做的是采集采样信号比如患者颅内的脑电信号，得到脑电图采样值，采样信号根据植入式电刺激装置的不同还可以是心电信号、神经电信号等。当前检测参数可以是医务工作人员根据患者病情进行初步判断后初始设定，当前检测参数一般包括检测算法、检测阈值、检测区间等，当前检测参数也可以选用检测设备的出厂默认值。步骤20：判断出采样信号超出预设的检测阈值；
在获取到采样信号后对采样信号进行处理，将采样信号与预设的检测阈值进行比较，以此判断是否对当前检测参数进行优化。预设的检测阈值的设置原则可以根据癫痫等事件的检测精准度和灵敏度进行设定。预设的检测阈值可以是医务工作人员进行设定，比如根据采样信号所包含的参数进行相应的设定，采样信号所包含的参数为多个时，对应的预设的检测阈值可以包含多个，当采样信号超出其中一个预设的检测阈值时即判断出采样信号超出预设的检测阈值。比如预设的检测阈值可以是癫痫等事件发生时的采样信号的50％。
步骤30：自响应获取最优检测参数；
在判断出采样信号超出预设的检测阈值后自响应获取最优检测参数，目的是采用最优检测参数替代当前检测参数。
最优检测参数可以是事先已经根据临床经验设定的检测参数，也可以是其它方式获取的检测参数，在此不做限定。
步骤40：基于最优检测参数调整当前检测参数。
基于获取的最优检测参数调整当前检测参数，植入式电刺激装置的微处理器可以发送参数调节指令和最优检测参数发送给采样电路，采样电路在接收到参数调节指令后执行检测参数调节指令，将最优检测参数替换掉当前检测参数后成为下一个检测参数采集采样信号。
通过上述技术方案，本申请提供的用于植入式电刺激装置的自响应检测参数优化方法，首先基于当前检测参数采集采样信号，在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，这里的检测阈值是基于采集的采样信号比如脑电信号判断是否为癫痫等病症发作或者即将发作时的脑电信号的范围值。如果当前检测参数设置的过小，在正常情况下有可能触发响应治疗，导致响应治疗的灵敏度过高，灵敏度过高也会增加副作用风险，因此在判断出采样信号超出预设的检测阈值后，自响应获取最优检测参数，并且基于获取到的最优检测参数调整当前检测参数，即将当前检测参数调整为最优检测参数。从而对当前检测参数做进一步优化，从而在对患者实时检测的过程中可以对癫痫等事件进行精准的检测，降低误检率的同时，触发响应治疗，达到精准治疗的目的，从而及时干预治疗防止癫痫等事件的发生。
总之，以上所述仅为本说明书的较佳实施例而已，并非用于限定本说明书的保护范围。凡在本说明书的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本说明书的保护范围之内。
上述一个或多个实施例阐明的系统、装置、模块或单元，具体可以由计算机芯片或实体实现，或者由具有某种功能的产品来实现。一种典型的实现设备为计算机。具体的，计算机例如可以为个人计算机、膝上型计算机、蜂窝电话、相机电话、智能电话、个人数字助理、媒体播放器、导航设备、电子邮件设备、游戏控制台、平板计算机、可穿戴设备或者这些设备中的任何设备的组合。
计算机可读存储介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态 随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于系统实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
上述对本说明书特定实施例进行了描述。其它实施例在所附权利要求书的范围内。在一些情况下，在权利要求书中记载的动作或步骤可以按照不同于实施例中的顺序来执行并且仍然可以实现期望的结果。另外，在附图中描绘的过程不一定要求示出的特定顺序或者连续顺序才能实现期望的结果。在某些实施方式中，多任务处理和并行处理也是可以的或者可能是有利的。

Claims (15)

  1. 一种用于植入式电刺激装置的自响应检测参数优化方法，包括：
    基于当前检测参数采集采样信号；
    判断出所述采样信号超出预设的检测阈值；
    自响应获取最优检测参数；
    基于所述最优检测参数调整所述当前检测参数。
  2. 如权利要求1所述的自响应检测参数优化方法，所述自响应获取最优检测参数，具体包括：
    自响应计算当前事件检测率；
    判断出所述当前事件检测率大于预设的目标检测率；
    自响应计算所述最优检测参数。
  3. 如权利要求1或2所述的自响应检测参数优化方法，基于当前检测参数采集采样信号之前，所述方法，还包括：
    将采样分析结果设置为检测标志。
  4. 如权利要求3所述的自响应检测参数优化方法，所述将采样分析结果设置为检测标志，具体包括：
    识别所述采样信号的波形特征；
    计算采样窗口期间所述采样信号的窗口参数；
    将所述波形特征和所述窗口参数作为采样分析结果，并且判断所述分析结果是否符合事件发生特征；
    如果是，则将所述采样分析结果设置为检测标志。
  5. 如权利要求4所述的自响应检测参数优化方法，在将所述分析结果设置为检测标志的条件下，所述基于当前检测参数采集采样信号，具体包括：
    基于不同的所述检测标志，采用对应的所述当前检测参数采集所述采样信号。
  6. 如权利要求4所述的自响应检测参数优化方法，识别所述采样信号的波形特征，具体包括：
    识别所述采样信号的半波数字信号，所述半波数字信号包括起始点和结束点，其中，所述起始点为脑电图采样值的初始脑电采样值，所述结束点为所述脑电图采样值的终点脑电采样值；
    计算所述半波数字信号的半波持续时间，所述半波持续时间为所述结束点对应时间点 与所述起始点对应时间点的差值。
  7. 如权利要求6所述的自响应检测参数优化方法，识别所述采样信号的半波数字信号，所述半波数字信号包括起始点和结束点，其中，所述起始点为脑电图采样值的初始脑电采样值，所述结束点为所述脑电图采样值的终点脑电采样值，具体包括：
    基于当前脑电图采样值和预设的磁滞值计算结束阈值；
    测量下一个脑电图采样值；
    将下一个脑电图采样值与所述结束阈值进行比较；
    如果下一个脑电图采样值超出所述结束阈值，则将所述下一个脑电图采样值的初始脑电采样值作为所述当前脑电图采样值的所述终点脑电采样值；
    计算所述半波数字信号的振幅，所述振幅为所述终点脑电采样值减去所述初始脑电采样值。
  8. 一种植入式电刺激装置，包括：
    采集模块，基于当前检测参数采集采样信号；
    判断模块，判断出所述采样信号超出预设的检测阈值；
    获取模块，自响应获取最优检测参数；
    调整模块，基于所述最优检测参数调整所述当前检测参数。
  9. 如权利要求8所述的装置，所述获取模块，具体用于：
    自响应计算当前事件检测率；
    判断出所述当前事件检测率大于预设的目标检测率；
    自响应计算所述最优检测参数。
  10. 如权利要求8或9所述的装置，所述装置还包括采样分析模块，所述采样分析模块，用于：
    将采样分析结果设置为检测标志。
  11. 如权利要求10所述的装置，所述采样分析模块，具体用于：
    识别所述采样信号的波形特征；
    计算采样窗口期间所述采样信号的窗口参数；
    将所述波形特征和所述窗口参数作为采样分析结果，并且判断所述分析结果是否符合事件发生特征；
    如果是，则将所述分析结果设置为检测标志。
  12. 如权利要求11所述的装置，所述采样分析模块，还具体用于：
    识别所述采样信号的半波数字信号，所述半波数字信号包括起始点和结束点，其中， 所述起始点为脑电图采样值的初始脑电采样值，所述结束点为所述脑电图采样值的终点脑电采样值；
    计算所述半波数字信号的半波持续时间，所述半波持续时间为所述结束点对应时间点与所述起始点对应时间点的差值。
  13. 如权利要求12所述的装置，所述采样分析模块，还具体用于：
    基于所述当前脑电图采样值和预设的磁滞值计算结束阈值；
    测量下一个脑电图采样值；
    将所述下一个脑电图采样值与所述结束阈值进行比较；
    如果所述下一个脑电图采样值超出所述结束阈值，则将所述下一个脑电图采样值的初始脑电采样值作为所述当前脑电图采样值的所述终点脑电采样值；
    计算所述半波数字信号的振幅，所述振幅为所述终点脑电采样值减去所述初始脑电采样值。
  14. 一种植入式电刺激系统，所述系统包括外部设备和如权利要求8至13中任一项所述的植入式电刺激装置，所述外部设备与所述植入式电刺激装置通信连接。
  15. 一种存储介质，用于计算机可读存储，所述存储介质存储有一个或者多个程序，所述一个或者多个程序可被一个或者多个处理器执行时，实现如权利要求1至7中任一项所述的自响应检测参数优化方法的步骤。
PCT/CN2022/073771 2021-02-05 2022-01-25 用于植入式电刺激装置的自响应检测参数优化方法及系统 WO2022166685A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP22748964.8A EP4289359A1 (en) 2021-02-05 2022-01-25 Self-responsive detection parameter optimization method and system for implantable electrical stimulation apparatus

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110164481.X 2021-02-05
CN202110164481.XA CN112842362B (zh) 2021-02-05 2021-02-05 用于植入式电刺激装置的自响应检测参数优化方法及系统

Publications (1)

Publication Number Publication Date
WO2022166685A1 true WO2022166685A1 (zh) 2022-08-11

Family

ID=75988744

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/073771 WO2022166685A1 (zh) 2021-02-05 2022-01-25 用于植入式电刺激装置的自响应检测参数优化方法及系统

Country Status (3)

Country Link
EP (1) EP4289359A1 (zh)
CN (1) CN112842362B (zh)
WO (1) WO2022166685A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116712672A (zh) * 2023-08-10 2023-09-08 北京析芒医疗科技有限公司 面向意识障碍康复的促醒电刺激方案确定方法及设备

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112842362B (zh) * 2021-02-05 2023-04-07 杭州诺为医疗技术有限公司 用于植入式电刺激装置的自响应检测参数优化方法及系统
CN117033988B (zh) * 2023-09-27 2024-03-12 之江实验室 基于神经电信号的癫痫样棘波处理方法和装置

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030004428A1 (en) * 2001-06-28 2003-01-02 Pless Benjamin D. Seizure sensing and detection using an implantable device
US20060235489A1 (en) * 2004-11-02 2006-10-19 Medtronic, Inc. Patient event marking in combination with physiological signals
US20080161713A1 (en) * 2006-12-27 2008-07-03 Kent Leyde Low Power Device With Variable Scheduling
US20080195166A1 (en) * 2007-02-09 2008-08-14 Felice Sun Devices and methods for monitoring physiological information relating to sleep with an implantable device
US20080319335A1 (en) * 2007-06-20 2008-12-25 Neuropace, Inc. System and method for automatically adjusting detection thresholds in a feedback-controlled neurological event detector
US20110270096A1 (en) * 2010-04-29 2011-11-03 Ivan Osorio Algorithm for detecting a seizure from cardiac data
CN109009085A (zh) * 2018-07-06 2018-12-18 四川斐讯信息技术有限公司 一种心电信号r波检测方法及系统
CN109475317A (zh) * 2016-07-27 2019-03-15 美敦力公司 用于在植入式医疗设备中的房性快速性心律失常检测的自动阈值
CN112842362A (zh) * 2021-02-05 2021-05-28 杭州诺为医疗技术有限公司 用于植入式电刺激装置的自响应检测参数优化方法及系统

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6415167B1 (en) * 2000-05-02 2002-07-02 Instrumentation Metrics, Inc. Fiber optic probe placement guide
CN101828917B (zh) * 2010-05-07 2011-09-14 深圳大学 心电信号特征提取的方法和系统
CN104225790B (zh) * 2014-09-19 2017-06-16 清华大学 闭环神经刺激系统
CN105068644A (zh) * 2015-07-24 2015-11-18 山东大学 一种基于卷积神经网络的p300脑电信号检测方法

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030004428A1 (en) * 2001-06-28 2003-01-02 Pless Benjamin D. Seizure sensing and detection using an implantable device
US20060235489A1 (en) * 2004-11-02 2006-10-19 Medtronic, Inc. Patient event marking in combination with physiological signals
US20080161713A1 (en) * 2006-12-27 2008-07-03 Kent Leyde Low Power Device With Variable Scheduling
US20080195166A1 (en) * 2007-02-09 2008-08-14 Felice Sun Devices and methods for monitoring physiological information relating to sleep with an implantable device
US20080319335A1 (en) * 2007-06-20 2008-12-25 Neuropace, Inc. System and method for automatically adjusting detection thresholds in a feedback-controlled neurological event detector
US20110270096A1 (en) * 2010-04-29 2011-11-03 Ivan Osorio Algorithm for detecting a seizure from cardiac data
CN109475317A (zh) * 2016-07-27 2019-03-15 美敦力公司 用于在植入式医疗设备中的房性快速性心律失常检测的自动阈值
CN109009085A (zh) * 2018-07-06 2018-12-18 四川斐讯信息技术有限公司 一种心电信号r波检测方法及系统
CN112842362A (zh) * 2021-02-05 2021-05-28 杭州诺为医疗技术有限公司 用于植入式电刺激装置的自响应检测参数优化方法及系统

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116712672A (zh) * 2023-08-10 2023-09-08 北京析芒医疗科技有限公司 面向意识障碍康复的促醒电刺激方案确定方法及设备
CN116712672B (zh) * 2023-08-10 2023-11-10 北京析芒医疗科技有限公司 面向意识障碍康复的促醒电刺激方案确定方法及设备

Also Published As

Publication number Publication date
EP4289359A1 (en) 2023-12-13
CN112842362B (zh) 2023-04-07
CN112842362A (zh) 2021-05-28

Similar Documents

Publication Publication Date Title
WO2022166685A1 (zh) 用于植入式电刺激装置的自响应检测参数优化方法及系统
US6549804B1 (en) System for the prediction, rapid detection, warning, prevention or control of changes in activity states in the brain of a subject
US7630757B2 (en) System for the prediction, rapid detection, warning, prevention, or control of changes in activity states in the brain of a subject
US20170065199A1 (en) Monitoring human brain excitability using synchronization measures
US20220096022A1 (en) System, method, and computer algorithm for measuring, displaying, and accurately detecting changes in electrophysiological evoked potentials
EP2222223B1 (en) Systems for managing heart rate dependent conditions
US9681835B2 (en) Detection of vestibular disorders based on vestibular noise
Andres et al. Finite dimensional structure of the GPI discharge in patients with Parkinson's disease
WO2022166684A1 (zh) 用于植入式电刺激装置的自适应闭环检测方法及系统
JP6957011B2 (ja) 睡眠段階判定装置、睡眠段階判定方法及びプログラム
CN114305346B (zh) 睡眠监测方法、装置、智能眼罩及存储介质
US20200315525A1 (en) Anesthesia stage identification and anesthesia depth calculation method and device
US20190209072A1 (en) Reliable seizure detection with a parallelizable, multi-trajectory estimate of lyapunov exponents
US20180014729A1 (en) System and Method for Detection of Cravings in Individuals with Addiction
CN109567750B (zh) 睡眠分期的确定方法、装置和计算机设备
EP3744386B1 (en) Electrical stimulation controlling device and electrical stimulation system
TWI833097B (zh) 判定疲憊指數之方法和設備
US20240032849A1 (en) Method and system for ssep (somatosensory evoked potentials) with monitorable baseline waveform determination
US20210386380A1 (en) Systems and methods for collecting retinal signal data and removing artifacts
WO2023186887A1 (en) Method for early illness detection using implanted neurostimulation system features
CN114786567A (zh) 用于检测基于心率模式的潮热的系统和方法
CN117224131A (zh) 一种智能手环、心电信号检测方法、装置及介质
CN112774034A (zh) 一种植入式闭环系统中电信号识别处理方法和装置
CN117545428A (zh) 用于可穿戴设备的患者癫痫发作的检测
CN116019422A (zh) 一种阻塞性呼吸暂停识别方法及相关设备

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22748964

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2022748964

Country of ref document: EP

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2022748964

Country of ref document: EP

Effective date: 20230905