WO2023029677A1

WO2023029677A1 - Closed-loop deep brain stimulation decision-making method, apparatus and system, and electronic device

Info

Publication number: WO2023029677A1
Application number: PCT/CN2022/099789
Authority: WO
Inventors: 王守岩; 刘伟; 宋睿; 聂英男; 李岩; 张晗
Original assignee: 复旦大学
Priority date: 2021-09-03
Filing date: 2022-06-20
Publication date: 2023-03-09

Abstract

Disclosed in the present application are a closed-loop deep brain stimulation decision-making method, system and apparatus, and an electronic device. The method comprises: respectively calculating corresponding stimulation parameters on the basis of a plurality of pieces of physiological signal data of a target object which is acquired in real time, wherein the plurality of pieces of physiological signal data comprise an intracranial local field potential signal, and further comprises at least one of a body surface physiological signal and a limb movement signal; performing data fusion on a first stimulation parameter corresponding to the intracranial local field potential signal, a second stimulation parameter corresponding to the body surface physiological signal and/or a third stimulation parameter corresponding to the limb movement signal of the acquired target object in the current time window, and obtaining corresponding target fused data; and determining whether the target fused data is greater than a target reference threshold value corresponding to the current time window, and if so, a deep brain stimulation needing to be performed. By means of the method, when a closed-loop control process meets the real-time performance, a stimulation decision-making data basis is more comprehensive in category and higher in strategic performance, such that the control precision of a closed-loop deep brain stimulation can be higher.

Description

Closed-loop deep brain stimulation decision-making method, device, system and electronic equipment

technical field

The present application relates to the field of medical electronic systems, in particular to a closed-loop deep brain stimulation decision-making method, system, device and electronic equipment.

Background technique

Deep brain stimulation (deep brain stimulation, DBS) has been widely used clinically in the treatment of movement disorders caused by Parkinson's disease and other diseases. Show good application prospects.

Traditional open-loop deep brain stimulation therapy has been clinically and practically proven to be used to treat Parkinson's, epilepsy, dystonia and other diseases and relieve the symptoms of these diseases. In recent years, closed-loop deep brain stimulation therapy has also been gradually developed. Compared with traditional open-loop deep brain stimulation therapy, closed-loop deep brain stimulation therapy has been proven to improve curative effect, reduce stimulation time and shorten the course of treatment, reduce power consumption of the stimulator, and self-adaption. It has the advantages of optimizing stimulation parameters and realizing personalized treatment.

Currently, there are many types of closed-loop deep brain stimulation systems, such as the authorized Chinese patent with the patent number CN201410481800.X, which discloses a closed-loop neural stimulation system that provides two closed-loop working modes, one of which is used for For long-term treatment, another working mode is used to verify the feasibility of closed-loop stimulation and closed-loop algorithm, and then realize the update and maintenance of the system. However, the closed-loop neural stimulation system has the disadvantages of limited number of recording channels and limited types of recording signals.

Therefore, it is necessary to find a method that can provide more physiological basis for closed-loop deep brain stimulation to improve stimulation accuracy.

Contents of the invention

The purpose of this application is to provide a closed-loop deep brain stimulation decision-making method, system, device and electronic equipment, which use a variety of physiological signals as the basis for brain stimulation decision-making, and can further improve the accuracy of deep brain stimulation under the premise of closed-loop.

In order to achieve the above-mentioned purpose of the invention, the application proposes the following technical solutions:

In a first aspect, a closed-loop deep brain stimulation decision-making method is provided, the method comprising:

Corresponding stimulation parameters are respectively calculated based on multiple physiological signal data of the target object acquired in real time; the multiple physiological signal data include intracranial local field potential signals, and at least one of body surface physiological signals and limb movement signals;

For the acquired first stimulation parameter corresponding to the intracranial local field potential signal within the current time window of the target object, and the second stimulation parameter corresponding to the body surface physiological signal and/or related to the limb movement performing data fusion on the third stimulus parameter corresponding to the signal and obtaining corresponding target fusion data;

It is judged whether the target fusion data is greater than the target reference threshold corresponding to the current time window, and if so, deep brain stimulation is required.

In a preferred embodiment, before the real-time acquisition of multiple physiological signal data of the target object, the method further includes configuring system parameters and performing system calibration, including:

Obtaining the brain tissue impedance of the target object;

Adjusting initialization parameters at system startup based on the brain tissue impedance to obtain initial parameters of the current system and performing system configuration;

After starting acquisition, self-calibrate the system and/or remove baseline drift.

In a preferred embodiment, the plurality of physiological signal data includes intracranial local field potential signal data, body surface physiological signal data and limb movement signal data;

The pair of acquired first stimulation parameters corresponding to the intracranial local field potential signal of the target object within the current time window, and second stimulation parameters corresponding to the body surface physiological signal and/or related to the Perform data fusion on the third stimulation parameters corresponding to the limb movement signals and obtain corresponding target fusion data, including:

Acquire the first stimulation parameter corresponding to the intracranial local field potential signal, the second stimulation parameter corresponding to the body surface physiological signal, and the third stimulation parameter corresponding to the limb movement signal in the current time window;

Perform a logical AND operation or a logical OR operation on the first stimulation parameter, the second stimulation parameter and the third stimulation parameter to obtain target fusion data.

In a preferred embodiment, before performing a logical AND operation or a logical OR operation on the first stimulation parameter, the second stimulation parameter and the third stimulation parameter to obtain the target fusion data, the performing data fusion on the acquired stimulation parameters corresponding to the plurality of physiological signal data of the target object within the current time window and obtaining corresponding target fusion data, and further including the obtained first stimulation parameters, the Preprocessing the second stimulation parameter and the third stimulation parameter includes:

The first stimulation parameter, the second stimulation parameter and the third stimulation parameter are positively valued respectively to obtain target positive value data, and the positive value includes obtaining the time domain amplitude in the time domain and calculating the absolute value or frequency domain to do short-time Fourier to obtain the frequency domain amplitude.

In a preferred embodiment, before the judging whether the target fusion data is greater than the target reference threshold corresponding to the current time window, the method further includes: configuring the target reference threshold in real time, including:

smoothing the obtained target fusion data in the time domain;

When the current time window is the first time window, configuring the target reference threshold as a preset experience threshold;

When the current time window is any time window except the first time window, all the first stimulation parameters, the second stimulation parameters and the At least one window average value of the target positive value data corresponding to the third stimulation parameter; arrange the at least one window average value in order and take the median value as the target reference threshold of the current time window.

In a preferred embodiment, the method also includes real-time monitoring of the target object, specifically including:

monitoring the brain current value output by the target object when performing deep brain stimulation;

When the brain current value exceeds the preset current threshold, it is determined that the deep brain stimulation needs to be stopped.

In the second aspect, a closed-loop deep brain stimulation decision-making system is also provided, the system includes implantable deep brain stimulation electrodes, terminal equipment, multiple wearable wireless physiological sensors and/or multiple wearable wireless motion sensors, and all A wearable wireless sensory stimulator connected to the implantable deep brain stimulation electrode; the local field potential physiological sensor includes a first micro control unit;

The wearable wireless sensory stimulator includes a local field potential physiological sensor, a control computing center, and a stimulation execution unit connected in sequence, and the local field potential physiological sensor, the stimulation execution unit are respectively connected with the implanted deep brain stimulation electrode connected to form a closed loop circuit;

The control computing center is based on the first stimulation parameter corresponding to the intracranial local field potential signal sent by the first micro-control unit, and the second stimulation parameter corresponding to the physiological signals of the plurality of wearable wireless physiological sensors and/or Perform data fusion on the third stimulation parameters corresponding to the motion signals of the plurality of wearable wireless motion sensors to obtain corresponding target fusion data, and determine whether the target fusion data is greater than the target reference threshold corresponding to the current time window, and if so, perform deep brain stimulation.

In a preferred embodiment, the local field potential physiological sensor further includes a recording channel switch, a stimulus artifact suppression circuit, and a first analog-to-digital converter connected in sequence, and the first micro-control unit and the second an analog-to-digital converter connection;

The recording channel switch is connected to the implantable deep brain stimulation electrode;

The input terminal of the first micro-control unit receives the converted intracranial local field potential signal output by the first analog-to-digital converter and outputs it to the input terminal of the control computing center so that the control computing center processes and obtains corresponding first stimulus parameter.

In a preferred embodiment, the stimulus artifact suppression circuit includes a preamplifier circuit, a high-pass filter circuit, a first low-pass filter circuit, and a post-amplifier circuit connected in sequence; wherein, the preamplifier circuit, the input The terminal is configured as a differential input, and the output terminal is configured as a single-ended output;

A high-pass filter circuit is connected to the preamplifier circuit, the input end is configured as a single-ended input, and the output end is configured as a single-ended output;

The first low-pass filter circuit is connected to the high-pass filter circuit, the input end is configured as single-ended input, and the output end is configured as single-ended output;

The post-amplification circuit is connected to the first low-pass filter circuit, the input end is configured as a single-ended input, and the output end is configured as a differential output.

In a preferred embodiment, the preamplifier circuit includes an instrumentation amplifier INA1, an adjustable gain resistor Rg1, and a ±5V DC stabilized voltage source, and the adjustable gain resistor Rg sets the voltage of the preamplifier circuit. The gain is 40dB or 60dB.

In a preferred embodiment, the high-pass filter circuit is configured as a three-stage 6-stage circuit, and each stage 2-stage circuit includes a capacitor Ch1, a resistor Rh1, a capacitor Ch2, a resistor Rh2, an operational amplifier Oph1, and ±5V DC power supply, the output end of the preamplifier circuit is connected to the P terminal of the operational amplifier Oph1 through the capacitor Ch1 and the capacitor Ch2 in turn, one end of the resistor Rh1 is connected between the capacitor Ch1 and the capacitor Ch2, and the other end is connected to the operational amplifier Oph1 One end of the resistor Rh2 is connected between the capacitor Ch2 and the P end of the operational amplifier Oph1, and the other end is grounded.

In a preferred embodiment, the first low-pass filter circuit is configured as a five-stage 10-stage circuit, and each stage 2-stage circuit includes a capacitor Cl1, a resistor Rl1, a capacitor Cl2, a resistor Rl2, an operational amplifier Opl1 and ±5V DC power supply, the output end of the high-pass filter circuit is connected to the P terminal of the operational amplifier Opl1 through the resistor Rl1 and the resistor Rl2 in turn, one end of the capacitor Cl1 is connected between the resistor Rl1 and the resistor Rl2, and the other end is connected to the terminal of the operational amplifier Opl1 The N terminal, one end of the capacitor Cl2 is connected between the resistor Rl2 and the P terminal of the operational amplifier Opl1, and the other end is grounded.

In a preferred embodiment, the post-amplification circuit includes an adjustable gain resistor Rf1, an adjustable gain resistor Rf2, a fully differential amplifier INA2, and a ±2.5V DC stabilized voltage source, and the adjustable gain resistor Rf1 Connected to the P input terminal and the P output terminal of the fully differential amplifier INA2, the adjustable gain resistor Rf2 is connected to the N input terminal and the N output terminal of the fully differential amplifier INA2, and the adjustable gain resistor Rg sets the The gain of the above-mentioned preamplifier circuit is 0dB-20dB.

In a preferred embodiment, the post-amplification circuit further includes a resistor Rb1, a resistor Rb2, and a matching resistor Rt, and the output terminal of the first low-pass filter circuit is connected to the P of the fully differential amplifier INA2 through the resistor Rb1. The input terminal, the N input terminal of the fully differential amplifier INA2 is grounded through the resistor Rb2, and the matching resistor Rt is connected to the input terminal of the post-amplification circuit.

In a preferred implementation manner, the post-amplification circuit further includes an output reference capacitor Cc and an output differential capacitor Cd, the output reference capacitor Cc is connected to the Voc pin of the fully differential amplifier INA2, and the output The differential capacitor Cd is connected between the P output terminal and the N output terminal of the fully differential amplifier INA2.

In a preferred implementation manner, the stimulation execution unit includes a stimulation circuit, and the stimulation circuit includes a constant voltage stimulation circuit and a constant current stimulation circuit arranged in parallel.

In a preferred embodiment, the stimulation execution unit further includes an impedance detection circuit, and the impedance detection circuit is arranged in parallel with the stimulation circuit.

In a preferred embodiment, the stimulation circuit further includes a monitoring circuit for monitoring the stimulation current in the constant voltage stimulation circuit or constant current stimulation circuit.

In a preferred embodiment, the wearable wireless physiological sensor includes a first signal acquisition circuit, a second micro-control unit connected to the first signal acquisition circuit, and a first communication unit;

The first communication unit is communicatively connected with the device terminal, so that the physiological signal collected by the first signal acquisition circuit received by the second micro-control unit is processed by the second micro-control unit and transmitted to the device terminal.

In a preferred embodiment, the first signal acquisition circuit includes an electrode interface connected in sequence, a second low-pass filter circuit, and a second analog-to-digital converter, and the electrode interface is in contact with the body surface of the human body to obtain physiological Signal.

In a preferred embodiment, the wearable wireless motion sensor includes a second signal acquisition circuit, a third micro-control unit connected to the second signal acquisition circuit, and a second communication unit;

The second communication unit is communicatively connected with the equipment terminal, so that the motion signal collected by the second signal acquisition circuit received by the third micro-control unit is processed by the third micro-control unit and transmitted to the device terminal.

In a preferred embodiment, the second signal acquisition circuit includes a 9-axis motion sensor, and the 9-axis motion sensor is in contact with a body part to acquire motion signals.

In a third aspect, a closed-loop deep brain stimulation decision-making device is provided, the device comprising:

The processing module is used to calculate corresponding stimulation parameters based on multiple physiological signal data of the target object acquired in real time; the multiple physiological signal data include intracranial local field potential signals, and also include body surface physiological signals and limb movement signals. at least one of

A fusion module, configured to acquire the first stimulation parameter corresponding to the intracranial local field potential signal within the current time window of the target object, and the second stimulation parameter corresponding to the body surface physiological signal and/or performing data fusion on the third stimulation parameter corresponding to the limb movement signal and obtaining corresponding target fusion data;

The judging module is used to judge whether the target fusion data is greater than the target reference threshold corresponding to the current time window, and if so, deep brain stimulation is required.

In a fourth aspect, an electronic device is provided, including:

one or more processors; and

A memory associated with the one or more processors, the memory is used to store program instructions, and when the program instructions are read and executed by the one or more processors, the execution of any one of the first aspect described method.

Compared with the prior art, the present application has the following beneficial effects:

This application provides a closed-loop deep brain stimulation decision-making method, system, device and electronic equipment, wherein the method is based on intracranial local field potential signals, body surface physiological signals, and limb movement signals for real-time judgment of whether deep brain stimulation is performed or not. Compared with the scheme in the prior art that only uses the intracranial local field potential signal as the basis for judgment, the closed-loop control process satisfies real-time performance, and the data basis for stimulation decision-making is more comprehensive and strategic, so that the closed-loop Higher precision of stimulus control;

Further, the closed-loop deep brain stimulation decision-making system provided by this application collects intracranial field electrical signals by setting a wearable wireless perceptual stimulator connected to implanted deep brain stimulation electrodes, and collects physiological signals and / or multiple wearable wireless motion sensors to collect multiple signals of motion signals to achieve strategic brain stimulation under multiple signals. Compared with the traditional scheme that only uses intracranial field potential signals as data basis, the closed-loop stimulation control accuracy is higher ; and, the system effectively realizes data storage, front-end display and online analysis by setting up device terminals and connecting them through communication, and improves the portability, portability, remote controllability and visualization of wearable devices, and is suitable for medical diagnosis and treatment control business;

And, in the closed-loop deep brain stimulation decision-making system provided by this application, the stimulation artifact suppression circuit set up adopts a fully differential low-noise structure with differential input and differential output, so that only one circuit needs to be set for one channel during acquisition, It can satisfy the single-ended input of the filter circuit and the differential input of the analog-to-digital conversion circuit at the same time, without considering the inconsistency of the two filters when the differential input is used; further, when setting more channels, only the same number of circuits needs to be added, which is the same as the existing Compared with existing technologies, half of the number of chips will be saved to achieve the purpose of reducing power consumption, reducing costs, and saving space;

It should be noted that the present application only needs to realize at least one of the above technical effects.

Description of drawings

Fig. 1 is the flowchart of the closed-loop deep brain stimulation decision-making method in the present embodiment;

Fig. 2 is a structural schematic diagram of the deep brain stimulation decision-making system in this embodiment;

Fig. 3 is another structural schematic diagram of the deep brain stimulation decision-making system in this embodiment;

Fig. 4 is a structural block diagram of the wearable wireless sensory stimulator in this embodiment;

Fig. 5 is a structural block diagram of the closed-loop deep brain stimulation artifact suppression circuit in this embodiment;

FIG. 6 is a schematic structural diagram of a preamplifier circuit in this embodiment;

FIG. 7 is a schematic structural diagram of one of the primary circuits of the high-pass filter circuit in this embodiment;

FIG. 8 is a schematic structural diagram of a first-stage circuit of the first low-pass filter circuit in this embodiment;

FIG. 9 is a schematic structural diagram of the post-amplification circuit in this embodiment.

Fig. 10 is a structural block diagram of the wearable wireless physiological sensor in the embodiment of this city;

Fig. 11 is a structural block diagram of the wearable wireless motion sensor in the present embodiment;

Fig. 12 is a schematic diagram of sensor settings when the deep brain stimulation decision-making system is used in this embodiment to implement deep brain stimulation decision-making;

Fig. 13 is another flow chart of the closed-loop deep brain stimulation decision-making method in this embodiment;

Fig. 14 is a schematic structural diagram of the closed-loop deep brain stimulation decision-making device in this embodiment.

Reference signs:

100-Closed-loop deep brain stimulation decision-making system, 10-Implantable deep brain stimulation electrodes/stimulation electrodes; 20-Terminal equipment; 30-Wearable wireless physiological sensors, 31-First signal acquisition circuit, 311-Electrode interface, 312- The second low-pass filter circuit, 313-the second analog-to-digital converter, 314-bias drive circuit, 32-the second micro-control unit, 33-the first communication unit, 34-electrostatic protection circuit, 35-the first serial port circuit , 36-first synchronization circuit, 37-second storage unit, 38-second power supply voltage stabilization circuit, 39-battery charge and discharge management circuit, 40-wearable wireless motion sensor, 41-second signal acquisition circuit, 411- 9-axis motion sensor, 42-third micro control unit, 43-second communication unit, 50-wearable wireless sensory stimulator, 51-local field potential physiological sensor, 511-recording channel switch, 512-preamplification circuit, 513-high-pass filter circuit, 514-first low-pass filter circuit, 515-post amplifier circuit, 516-first analog-to-digital converter, 517-first micro-control unit, 52-control computing center, 53-stimulus execution unit , 531 - stimulation circuit, 532 - impedance detection circuit, 533 - monitoring circuit, 534 - stimulation channel switch, 54 - battery management circuit, 55 - first power supply voltage stabilization circuit, 56 - first storage unit.

Detailed ways

In order to make the purpose, technical solutions and advantages of the application clearer, the technical solutions in the embodiments of the application will be clearly and completely described below in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are only Some embodiments of this application are not all embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

In the description of the present application, it should be understood that the terms "first" and "second" are used for description purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, a feature defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the present application, unless otherwise specified, "plurality" means two or more.

Closed-loop deep brain stimulation is to record the deep brain field potential signal and regulate brain function in real time by implanting electrodes, and establish an individualized regulation method based on disease phenotype, which is of great importance for basic research on brain movement, cognition, emotion, memory, etc. value. There are about 14 billion nerve cells in the human cerebral cortex, which control various functions such as movement and sensation of the human body. Therefore, closed-loop deep brain stimulation requires very high accuracy of brain stimulation. Currently, human physiological signals that can be collected include intracranial local field potential signal data, etc. If effective decision-making and stimulation can be made based on various physiological signal data, compared with the current method based only on intracranial local field potential signal data, Stimulus execution precision will be more precise. Therefore, this embodiment provides a closed-loop deep brain stimulation decision-making method, which can effectively achieve the above effects.

The closed-loop deep brain stimulation decision-making method, system, device and electronic equipment of this embodiment will be further described in detail below with reference to FIGS. 1-14 .

Example

As shown in Figure 1, this embodiment provides a closed-loop deep brain stimulation decision-making method, including the following steps:

S1. Calculate corresponding stimulation parameters based on multiple physiological signal data of the target object acquired in real time; the multiple physiological signal data include intracranial local field potential signals, and at least one of body surface physiological signals and limb movement signals.

Among them, the intracranial local field potential signal data (LFP) is collected by the local field potential physiological sensor set by the wearable wireless sensory stimulator. Physiological signal data on the body surface is collected by wearable wireless physiological sensors, including but not limited to electrocardiographic signals, electromyographic signals, scalp electroencephalograms, oculoelectric signals and other signals related to physiological states of the target object. Body motion signal data is obtained through wearable wireless motion sensors, including but not limited to acceleration, angular velocity, displacement, swing, rotation angle, magnetic force and other motion-related signals when any body part of the target object moves.

Stimulation parameters are transmitted from the stimulation execution unit to the implanted deep brain stimulation electrode and stimulate the stimulation voltage or current when performing intracranial stimulation, mainly including amplitude, pulse width, frequency, delay time and waveform type, etc. The corresponding stimulation parameters derived from the signals can be realized by using existing algorithms or corresponding relationships, which are not limited in this embodiment. It should be noted that what is obtained in step S1 is the stimulation data corresponding to the physiological signals obtained by each type of sensor, rather than the stimulation parameters for the final electrical stimulation performed by this method.

Before step S1, the method also includes:

Sa, configure system parameters and perform system calibration, including:

Sa1. The local field potential physiological sensor acquires the brain tissue impedance of the target object;

Sa2, based on the brain tissue impedance, adjust the initialization parameters when the system starts to obtain the initial parameters of the current system and perform system configuration;

Sa3. After the acquisition is started, the system is self-calibrated and/or the baseline drift is removed.

It should be noted that, before performing brain stimulation, after completing the wearing, starting and parameter initialization of the wearable wireless sensory stimulator, wearable wireless physiological sensor and/or wearable wireless motion sensor, through the above steps Sa1 and Sa2 based on Brain tissue impedance configures the initial system parameters. The initial system parameters include the acquisition parameters (sampling rate, gain, channel, precision) of each part of the sensor in step S1 when acquiring signals, and the preset stimulation parameters for final brain stimulation (stimulation voltage or current). Amplitude, pulse width, frequency, delay time, waveform type, etc.), data storage parameters (space size, rate, channel), voltage safety threshold and current safety threshold for subsequent stimulation monitoring.

After the system configuration is completed, self-calibration and/or baseline drift removal of the wearable wireless physiological sensor and/or wearable wireless motion sensor, local field potential physiological sensor is performed.

Of course, up to step S1, none of the stimulation parameters corresponding to the acquired multiple physiological signals is used as a data basis for closed-loop deep brain stimulation, and the data is not stored.

S2. For the acquired target object within the current time window, the first stimulation parameter corresponding to the intracranial local field potential signal, and the second stimulation parameter corresponding to the body surface physiological signal and/or the third stimulation corresponding to the limb movement signal parameters for data fusion and obtain the corresponding target fusion data. Specifically, step S2 includes:

S21. Obtain the first stimulation parameter corresponding to the intracranial local field potential signal, the second stimulation parameter corresponding to the body surface physiological signal, and the third stimulation parameter corresponding to the limb movement signal in the current time window;

S22. Perform logical AND operation or logical OR operation on the first stimulation parameter, the second stimulation parameter and the third stimulation parameter to obtain target fusion data.

In this embodiment, by fusing the stimulation parameters corresponding to two or more types of physiological signal data to obtain the target fusion data, it can fully realize the change of the data type about the physiological state of the target object used in closed-loop deep brain stimulation. It is more comprehensive and more decision-making, and the closed-loop control performed has higher precision.

In this step, the real-time collected data is synchronously stored while performing real-time calculation. It should be noted that different sensors have different storage methods. In view of the fact that the sensors in this embodiment are all wearable devices, as an exemplary implementation, the intracranial local field potential signal data is stored in the SD card configured by the wearable wireless sensory stimulator, and the body surface physiological signal data and limb The motion signal data are respectively stored in the TF card configured by the corresponding sensor equipment, and all the sensors store the collected data to the user terminal or cloud by wireless transmission. The way of front-end display and receive manual intervention on the user side, in order to achieve remote control.

In a preferred embodiment, steps S21, S22 also include:

S2a. Perform preprocessing on the obtained first stimulation parameter, the second stimulation parameter and the third stimulation parameter, specifically including:

On this basis, the above-mentioned step S22 is actually: perform logical AND operation or logical OR operation on the target positive value data respectively corresponding to the first stimulation parameter, the second stimulation parameter and the third stimulation parameter to obtain the target fusion data .

S3. Determine whether the target fusion data is greater than the target reference threshold corresponding to the current time window. It should be noted that the target reference threshold in this embodiment is not fixed, but is set individually corresponding to the time window, so as to adjust in real time as the physiological state of the target object changes, further improving the closed-loop control accuracy.

Therefore, before step S3 and after step S2, the method also includes Sb, configuring the target reference threshold in real time, including:

Sb1, smoothing the obtained target fusion data in the time domain;

When the current time window is the first time window, execute step Sb2;

When the current time window is any time window except the first time window, execute steps Sb3-Sb4;

Sb2. Configure the target reference threshold as a preset experience threshold; the preset experience value may be an artificially set experience value or an experience threshold calculated by the system according to previous stimulation parameters, which is not limited in this embodiment.

Sb3. Obtain at least one window average value of target positive value data corresponding to all the first stimulation parameters, the second stimulation parameters, and the third stimulation parameters in each time window before the current time window;

Sb4. Arrange the at least one window average value in sequence and take the median value as the target reference threshold of the current time window.

Exemplarily, the target fusion data is smoothed in the time domain, and the time window is 20 ms, that is, every 20 ms is based on the target corresponding to the first stimulation parameter, the second stimulation parameter and the third stimulation parameter in the current time window Positive value data obtains at least one window average value, sorts all obtained window average values in ascending or descending order, and takes the median value as the target reference threshold.

Of course, if the judgment result of step S3 is yes, the system will apply electrical stimulation with preset stimulation parameters to the target object through the implanted deep brain stimulation electrodes. The preset stimulation parameters in this step are the preset stimulation parameters configured in step Sa2 based on the brain tissue impedance of the target subject. How to configure the stimulation parameters based on brain tissue impedance can be realized by common technical means in the field, which is not limited in this embodiment. Since this technical means is not an improvement point of this embodiment, no further description will be made in this embodiment.

Of course, if the judgment result is negative, then the implanted deep brain stimulation electrode will remain in a non-stimulating state.

And, while performing deep brain stimulation, the method also includes:

Sc. Real-time monitoring of the electrical stimulation performed, specifically including:

Monitor the corresponding brain stimulation current value when outputting electrical stimulation;

When the brain stimulation current exceeds the preset current threshold, the application of electrical stimulation to the target object through the implanted deep brain stimulation electrodes is stopped.

Of course, in a preferred embodiment, when the system sets the user terminal, when a sudden system failure occurs, the stimulation can be manually terminated by means of receiving manual intervention.

This embodiment provides a closed-loop deep brain stimulation decision-making method, which is based on intracranial local field potential signal data, body surface physiological signal data, and limb movement signal data to judge whether deep brain stimulation is performed in real time. In the technology, only the intracranial local field potential signal is used as the basis for judgment. The closed-loop control process satisfies real-time performance, and the stimulation data basis is more comprehensive and strategic, and the closed-loop stimulation control accuracy is higher.

This embodiment also provides a closed-loop deep brain stimulation decision-making system. The closed-loop deep brain stimulation decision-making system includes a wearable wireless physiological sensor and/or a wearable wireless motion sensor, a wearable wireless sensory stimulator and an implanted deep brain stimulation electrode, wherein the wearable wireless sensory stimulator includes an integrated set Local Field Potential Physiological Sensor and Processing Components. Among them, the local field potential physiological sensor is used to monitor the intracranial local field potential signal of the target object; the wearable wireless physiological sensor is used to monitor the body surface physiological signal of the target object; the wearable wireless motion sensor is used to monitor the limb movement signal; Type deep brain stimulation electrodes are used to perform electrical stimulation on a target subject.

In one embodiment, as shown in FIG. 2 , the wearable wireless physiological sensor and the wearable wireless motion sensor are respectively connected to the processing component in communication. In this connection mode, the processing components include a control computing center, a stimulus execution unit and a storage unit. That is, the wearable wireless sensory stimulator integrates local field potential physiological sensors, a control computing center, a stimulus execution unit, and a storage unit, where the storage unit is used to store the data collected by each sensor.

In the second embodiment, as shown in FIG. 3, the system also includes a terminal device (PC host computer), a wearable wireless physiological sensor and/or a wearable wireless motion sensor, a local field potential physiological sensor, and a processing component respectively connected with User terminal communication connection. The user terminal acquires multiple physiological signal data transmitted by each sensor in real time, which can be used for online analysis, and provides offline analysis after data storage. In this connection mode, the processing components include a control computing center and a stimulus execution unit.

It should be noted that the difference between the above two implementations is only whether the user terminal is integrated into the wearable wireless sensory stimulator. When the first implementation is adopted, the system can be highly integrated and the portability of the system can be improved. ; When the second implementation mode is adopted, while achieving portability, it further provides a human-computer interaction interface to visualize information and improve remote controllability, which is suitable for remote medical diagnosis and treatment, and is more suitable for remote or cloud medical applications middle.

The closed-loop deep brain stimulation decision-making system in this embodiment will be further described in detail below by taking the second implementation as an example.

As shown in Figure 3, the present embodiment provides a closed-loop deep brain stimulation decision-making system 100 (hereinafter referred to as the system), the system 100 includes an implantable deep brain stimulation electrode 10 (hereinafter referred to as the stimulation electrode), a terminal device 20, a plurality of A wearable wireless physiological sensor 30 and/or a plurality of wearable wireless motion sensors 40 , and a wearable wireless sensory stimulator 50 connected to the implanted deep brain stimulation electrode 10 . A plurality of wearable wireless physiological sensors 30 and/or a plurality of wearable wireless motion sensors 40, a wearable wireless sensory stimulator 50 are respectively connected to the terminal device 20 in wireless communication, and a plurality of wearable wireless physiological sensors 30, a plurality of wearable wireless physiological sensors The wireless motion sensors 40 are respectively connected in communication with the wearable wireless sensory stimulator 50 .

That is to say, the system 100 takes the wearable wireless sensory stimulator 50 as the core, configures the implantable deep brain stimulation electrode 10 for deep brain stimulation, and configures the terminal device 20 as a data transmission node, and further sets up multiple acquisition signals. The sensors collect physiological signals and/or motion signals. As a preferred implementation, the system 100 is provided with 3 to 8 wearable wireless physiological sensors 30, which can collect differential physiological signals of 24 to 80 channels; and/or, at least 5 to 16 wearable wireless motion sensors 40 are provided, It can collect motion signals from 5 to 16 test points.

Therefore, the system 100 includes three schemes, scheme one: the system 100 also includes a plurality of wearable wireless physiological sensors 30; scheme two: the system 100 also includes a plurality of wearable motion sensors 40; scheme three: the system 100 also includes multiple wearable wireless physiological sensors 30; A wearable wireless physiological sensor 30 and a plurality of wearable motion sensors 40. It should be noted that the structures of the above three solutions are relatively similar, and all of them are within the protection scope of the present application. In this embodiment, scheme three is taken as an example for further specific description. In addition, it should be noted that, when adopting scheme 1 or scheme 2, it is only necessary to remove the corresponding multiple sensors not included on the basis of scheme 3, and of course the corresponding signal type does not need to be considered either.

The wearable wireless sensory stimulator 50 is used to collect intracranial local field potential (LFP) physiological signals. Wearable wireless physiological sensors are used to collect body surface physiological signals, including but not limited to electrocardiographic signals, electromyographic signals, scalp EEG, eye electricity, etc. Wearable wireless motion sensors are used to collect body surface motion signals, including but not limited to acceleration, angular velocity, displacement, swing, rotation angle, magnetic force and other motion information of the limbs.

Continuing to refer to FIG. 4 , the wearable wireless sensory stimulator 50 is the core device of the system, which includes a local field potential physiological sensor 51 , a control computing center 52 and a stimulation execution unit 53 connected in sequence. The local field potential physiological sensor 51 includes one, and its input terminal is connected with the stimulating electrode 10 to obtain the intracranial field potential signal collected by the stimulating electrode. The system 100 in this embodiment includes two stimulating electrodes 10, which are respectively implanted in the left and right brains. The output end of the stimulation execution unit 53 is connected with the stimulation electrode 10 to perform electrical stimulation according to the received stimulation instruction. Therefore, as far as the wearable wireless sensory stimulator 50 is concerned, the physiological sensor 51 is connected to the stimulating electrode 10 based on the local field potential, and the stimulation execution unit 53 is connected to the stimulating electrode 10 to form a closed-loop circuit.

Further, the local field potential physiological sensor 51 includes a recording channel switch 511, a stimulation artifact suppression circuit, a first analog-to-digital converter 516, and a first micro-control unit 517 connected in sequence, wherein the stimulation artifact suppression circuit includes a preamplifier The circuit 512, the high-pass filter circuit 513, the first low-pass filter circuit 514, and the post-amplification circuit 515, wherein the pre-amplifier circuit has a differential input at its input and a single-ended output at its output.

Specifically, as shown in FIG. 4 , the recording channel switch 511 is connected to the stimulating electrode 10 , and the recording channel switch 511 adjusts acquisition parameters by selecting different contacts of the stimulating electrode 10 . Specifically, the recording channel switch 511 realizes the above functions by setting a dial switch matrix. The pre-amplification circuit 512 can freely adjust the magnification factor from 1 to 10000 times. For the convenience of calculation, this system mainly adopts two modes of 100 times and 1000 times. The high-pass filter circuit 513 is composed of a second-order fully differential passive filter circuit and a sixth-order Butterworth active filter circuit, and is used to eliminate low-frequency common-mode noise and low-frequency differential-mode noise, with a cutoff frequency of 0.5 Hz. The first low-pass filter circuit 514 is a 10th-order Butterworth active filter composed of a 2nd-order Sallen-key structure with a cutoff frequency of 45 Hz. The first analog-to-digital converter 516 uses a chip ADS1299 with a large dynamic range and high signal-to-noise ratio, which has a resolution of 24 bits, meets the measurement requirements of a large dynamic range, and can measure a minimum local field potential signal of 1uVpp. The input terminal of the first micro-control unit 517 receives the converted intracranial field potential signal output by the first analog-to-digital converter 516 and outputs it to the input terminal of the control computing center 52 so that the control computing center 52 processes and obtains corresponding first stimulation parameters. The first microcontroller unit 517 adopts a wireless microcontroller, which integrates a wireless antenna to facilitate the terminal device 20 to perform wireless data transmission. The amplified data is converted by the first analog-to-digital converter 516 and then collected by the wireless microcontroller and sent to the controller computing center or sent to the terminal device 202 for display. Preferably, the wireless microcontroller of the first microcontroller unit 517 is CC3220SFMODA. The terminal device 20 includes but is not limited to a PC host computer, which can not only realize terminal display, but also perform remote control.

The stimulus artifact suppression circuit in this embodiment adopts a fully differential low-noise structure with differential input and differential output. When collecting, only one circuit is required for one channel, and the single-ended input and analog-to-digital conversion of the filter circuit can be satisfied at the same time. The differential input of the circuit does not need to consider the inconsistency of the two filters when the differential input is used. The stimulation artifact suppression circuit will be further described in detail below.

As shown in Figure 5, it is a preferred embodiment of the stimulus artifact suppression circuit of this embodiment, the stimulus artifact suppression circuit includes a preamplifier circuit 512, a high-pass filter circuit 513, a first low-pass filter circuit connected in sequence 514, and a post-amplification circuit 515, wherein the input end of the pre-amplification circuit 512 is configured as a differential input, and the output end is configured as a single-ended output; the input end of the high-pass filter circuit 513 is configured as a single-ended input, and the output end is configured as a single-ended input terminal output; the input terminal of the first low-pass filter circuit 514 is configured as single-ended input, and the output terminal is configured as single-ended output; the input terminal of the post amplifier circuit 515 is configured as single-ended input, and the output terminal is configured as differential output. The entire closed-loop deep brain stimulation stimulation artifact suppression circuit is configured as a fully differential low-noise structure with differential input and differential output.

Specifically, as shown in FIG. 6 , the preamplifier circuit 512 includes an instrumentation amplifier INA1 , an adjustable gain resistor Rg1 , a ±5V DC regulated voltage source, and peripheral capacitors. The instrumentation amplifier INA1 is powered by ±5V. The instrumentation amplifier INA1 has the advantages of low offset voltage and low output noise, which can meet the minimum noise requirements for neural signal recording.

The instrument amplifier INA1 sets the amplification factor through the adjustable gain resistor Rg1, and the gain configuration of the entire preamplifier circuit 512 is 40dB to 60dB. Preferably, when the resistance value of the adjustable gain resistor Rg1 is set to 60.4Ω, the preamplifier circuit 512 The gain of the preamplifier circuit 512 is 40dB; when the resistance value of the adjustable gain resistor Rg1 is set to 6.04Ω, the gain of the preamplifier circuit 512 is 60dB.

The cut-off frequency of the high-pass filter circuit 513 is configured as 0.05-1 Hz, which can well filter out low-frequency signals that do not need to be collected. As shown in Figure 7, the high-pass filter circuit 513 is preferably designed with -3dB passband ripple, the cut-off frequency is 0.5Hz, the stopband frequency is set to 0.1Hz, and the attenuation is -100dB. According to the second-order sallen-key structure, the third-order and sixth-order For the Butterworth type circuit, of course, in order to reduce noise, the high-pass filter circuit 513 does not introduce a resistor amplification factor, and the configuration amplification factor is 1. The structure of the 2-order high-pass filter circuit of each stage is the same (Fig. 7 shows the specific structure of one of them), and the first-stage 2-order high-pass filter circuit includes capacitor Ch1, resistor Rh1, capacitor Ch2, resistor Rh2, operational amplifier Oph1 and ±5V DC power supply, the operational amplifier Oph1 uses ±5V power supply, the output terminal of the instrumentation amplifier INA1 is connected to the P terminal of the operational amplifier Oph1 through the capacitor Ch1 and the capacitor Ch2 in turn, one end of the resistor Rh1 is connected between the capacitor Ch1 and the capacitor Ch2, and the other end Connect to the N terminal of the operational amplifier Oph1, one end of the resistor Rh2 is connected between the capacitor Ch2 and the P terminal of the operational amplifier Oph1, and the other end is grounded. Further, by analogy, the second-stage 2nd-order high-pass filter circuit includes capacitor Ch3, resistor Rh3, capacitor Ch4, resistor Rh4, operational amplifier Oph2, and ±5V DC power supply; the third-stage 2nd-order high-pass filter circuit includes capacitor Ch5, resistor Rh5, capacitor Ch6, resistor Rh6, operational amplifier Oph3, and ±5V DC power supply, among which, the first and second stages are preferably completed with low-noise, low-offset voltage dual op-amp ADA4522-2, and the third stage is completed by low-noise, low-offset voltage The single op amp ADA4522-1 is completed. The high-pass filter circuit 513 is set in this way, and its filtered output noise is less than 1.1uVpp.

The cut-off frequency of the first low-pass filter circuit 514 is configured as 40-48 Hz, which can well obtain the local field potential signal in the β frequency band, especially configured as the best cut-off frequency of 45 Hz. The first low-pass filter circuit 514 is preferably an active low-pass filter circuit, which can better reduce input and output noise, thereby reducing total noise, and has a simple structure, a steep drop at the cutoff frequency, and good filtering effect. Specifically, as shown in Figure 8, the first low-pass filter circuit 514 is preferably designed with -3dB passband ripple, the cutoff frequency is 45Hz, and the stopband frequency is set to 125Hz. According to the second-order Sallen-key structure, five-stage 10-order Butterworth type circuit, similarly, in order to reduce noise, the low-pass filter circuit does not introduce a resistor magnification, and the configuration magnification is 1. The structure of the second-order low-pass filter circuit of each stage is the same (Fig. 8 shows the specific structure of one of them), and the first-stage second-order low-pass filter circuit includes capacitor Cl1, resistor Rl1, capacitor Cl2, resistor Rl2, operational amplifier Opl1 and ±5V DC power supply, the operational amplifier Opl1 adopts ±5V power supply, the output terminal of the high-pass filter circuit is connected to the P terminal of the operational amplifier Opl1 through the resistor Rl1 and the resistor Rl2 in turn, and one end of the capacitor Cl1 is connected between the resistor Rl1 and the resistor Rl2, The other end is connected to the N terminal of the operational amplifier Opl1, one end of the capacitor Cl2 is connected between the resistor Rl2 and the P terminal of the operational amplifier Opl1, and the other end is grounded. Further, by analogy, the second-stage 2nd-order low-pass filter circuit includes capacitor Cl3, resistor Rl3, capacitor Cl4, resistor Rl4, operational amplifier Opl2, and ±5V DC power supply; the third-stage 2nd-order low-pass filter circuit includes capacitor Cl5, resistor Rl5 , capacitor Cl6, resistor Rl6, operational amplifier Opl3 and ±5V DC power supply; the fourth-stage 2-order low-pass filter circuit includes capacitor Cl7, resistor Rl7, capacitor Cl8, resistor Rl8, operational amplifier Opl4 and ±5V DC power supply; the fifth stage The second-order low-pass filter circuit includes capacitor Cl9, resistor Rl9, capacitor Cl10, resistor Rl10, operational amplifier Opl5 and ±2.5V DC power supply. Among them, the first stage, the second stage, the third stage, and the fourth stage are preferably completed by the low-noise, low-offset voltage dual op-amp ADA4522-2, and the fifth stage is completed by the low-noise, low-offset voltage single-op-amp ADA4522- 1 done. The first low-pass filter circuit 514 is set in such a way that the filtered output noise is less than 1.6uVpp.

As shown in FIG. 9, the post-amplification circuit 515 includes a resistor Rb1, an adjustable gain resistor Rf1, a resistor Rb2, an adjustable gain resistor Rf2, a matching resistor Rt, a fully differential amplifier INA2, an output reference capacitor Cc, and an output differential capacitor Cd, ± 2.5V DC regulated voltage source and decoupling capacitor. The fully differential amplifier INA2 uses the fully differential amplifier HP210 to achieve a low input offset voltage of 10uV and an output noise of 9nV/√Hzf=1k to meet the minimum noise requirements for neural signal recording; further, the adjustable gain resistor Rf1 is connected to the fully differential amplifier The P input terminal and P output terminal of INA2, the adjustable gain resistor Rf2 are connected to the N input terminal and N output terminal of the fully differential amplifier INA2, and the amplification factor is set through the adjustable gain resistor Rf1 and the adjustable gain resistor Rf2, and the entire post-amplification The gain configuration of the circuit 515 is 0dB-20dB, and the gains of the post-amplification circuit 515 and the pre-amplification circuit 512 cooperate to achieve a total gain of 40dB-80dB. When the resistance values of the adjustable gain resistor Rf1, the adjustable gain resistor Rf2, the resistor Rb1, and the resistor Rb2 are all set to 215Ω, the gain of the post amplifier circuit 515 is 0dB; when the adjustable gain resistor Rf1 and the adjustable gain resistor Rf2 When the resistance value is set to 1000Ω, and the resistance values of the resistors Rb1 and Rb2 are set to 100Ω, the gain of the post amplifier circuit 515 is 20dB.

The output terminal of the first low-pass filter circuit 514 is connected to the P input terminal of the fully differential amplifier INA2 through the resistor Rb1, and the N input terminal of the fully differential amplifier INA2 is grounded through the resistor Rb2 to realize single-ended input of the fully differential amplifier INA2. The Voc pin of the fully differential amplifier INA2 can set the default mid-supply reference of the output terminal, and an output reference capacitor Cc is added to the Voc pin to reduce other high output noise of the internal high impedance bias; the P output of the fully differential amplifier INA2 Add an output differential capacitor Cd between the N output terminal and the N output terminal to filter out high-frequency components and enter the subsequent analog-to-digital conversion circuit; since the output terminal of the post-amplification circuit 515 is configured as a differential output, it is necessary to use the input terminal of the post-amplification circuit 515 Add a matching resistor Rt, and do a good job of impedance matching related to the actual gain by selecting an appropriate matching resistor Rt. If the signal has clipping or excessive attenuation, it means that there is a mismatch and the gain setting is incorrect.

Different from the previous circuits, the voltage of the fully differential amplifier INA2 needs to match the voltage of the analog-to-digital conversion circuit for subsequent digitization, especially the maximum voltage input range of the analog-to-digital conversion circuit to prevent saturation of the analog-to-digital conversion circuit . Specifically, when the fully differential amplifier INA2 is preferably powered by ±2.5V, the maximum output voltage of the post-amplification circuit 515 is determined to be ~5Vpp. Therefore, after the signal passes through the post-amplification circuit 515, the amplification factor is generally set to 0dB according to requirements. ~20dB, the maximum is 20dB. When the setting is higher than 0dB, it needs to be considered in conjunction with the gain of the analog-to-digital conversion circuit. The peak-to-peak amplitude of the signal cannot exceed the maximum voltage range of the analog-to-digital conversion circuit.

In the stimulation artifact suppression circuit in this embodiment, when the nerve signal is collected, the nerve signal is input to the preamplifier circuit 512 in a differential manner. At this time, the nerve signal includes a weaker local field potential signal in the brain and the stimulation signal itself A relatively strong stimulus artifact signal is generated. After being amplified by the preamplifier circuit 512, the 100uVpp-level local field potential signal is amplified to 10mVpp-100mVpp after passing through, and the maximum 10mV-level stimulus artifact signal is amplified to a maximum of 1Vpp-10Vpp. The neural signal is output to the high-pass filter circuit 513 in a single-ended manner; after the amplified neural signal is filtered by high-pass filtering, the baseline drift and low-frequency DC components are filtered out. At this time, the stimulus artifact signal and its harmonic components are attenuated, roughly as ~10Vpp level, while the local field potential signal is also attenuated, but still at ~100mVpp level, various high-frequency components of the mixed signal are output to the first low-pass filter circuit 514 in a single-ended manner; the high-frequency components of the mixed signal After low-pass filtering, the frequency components of stimulus artifacts and their harmonic signals will be greatly attenuated by about -100dB, and will be suppressed from ~10Vpp level to 100uVpp level, while the local field potential signal will only be slightly attenuated by about -3dB, which is still ～100mVpp level, suppress the stimulation artifact signal and leave a useful local field potential signal. The cleaner is that the local field potential signal is output to the next post-amplification circuit 515 in a single-ended manner; the local field potential signal passes through the post-amplification circuit After the 515 is amplified, the maximum amplitude of the output is ~1000mVpp level. After the single-ended signal is converted into a differential signal, it is filtered by the output differential capacitor Cd and output to the next-stage analog-to-digital conversion circuit.

Continuing to refer to FIG. 4 , the stimulation execution unit 53 includes a stimulation circuit 531 , an impedance detection circuit 532 , a monitoring circuit 533 and a stimulation channel switch 534 , wherein the stimulation circuit 531 and the impedance detection circuit 532 are arranged in parallel.

Specifically, the stimulation circuit 531 is used to output stimulation voltage or stimulation current, including a constant voltage stimulation circuit and a constant current stimulation circuit arranged in parallel, and the stimulation mode is selected by the stimulation channel switch 533 as constant voltage stimulation or constant current stimulation. The constant current stimulation is realized by the first analog-to-digital converter inside the controller to realize the voltage output, and the constant current driving circuit module outputs the waveform to implement the stimulation. Among them, the constant voltage stimulation circuit is controlled by the controller SPI to control the external 16bits digital-to-analog converter AD5761 to realize the voltage output, and the constant voltage drive circuit module outputs the waveform to implement the stimulation.

The impedance detection circuit 532 sets an impedance channel switch to control the on-off of the circuit. In the specific use of the system 100, before performing deep brain stimulation, it is necessary to first detect the intracranial impedance through the impedance detection circuit 532 to configure the stimulation parameters, so whether to perform impedance detection or not is controlled by the on-off control of the impedance channel switch.

The monitoring circuit 533 is used to monitor the magnitude of the stimulation current in the constant voltage stimulation circuit or the constant current stimulation circuit during stimulation execution. Specifically, the monitoring circuit 533 includes a dual threshold comparator. When the current is lower than a certain threshold or higher than a certain threshold, Both will send out an alarm signal and pass through the IO detection signal of the controller.

Of course, the wearable wireless sensory stimulator 50 also includes a battery management circuit 54 and a first power supply voltage stabilization circuit 55 . The battery management circuit 54 can realize charging and discharging management of the battery to prolong the battery life of the wearable device. The first power supply voltage regulator circuit 55 is used to provide low noise and low ripple voltage rails for the system, including +5.5V, -5.5V, +5V, -5V, +3.3V, +2.5V, -2.5V and other systems to the positive and negative voltage rails.

And, the wearable wireless sensory stimulator 50 also includes a first storage unit 56 for storing data locally, and the first storage unit 56 preferably adopts an SD card.

The above is the specific structural description of the wearable wireless sensory stimulator 50 , on this basis, the wearable wireless physiological sensor 30 and the wearable motion sensor 40 will be further described in detail below.

As shown in Figure 10 is a structural block diagram of a wearable wireless physiological sensor 30, as shown in the figure, the wearable wireless physiological sensor 30 includes a first signal acquisition circuit 31, a second micro control unit 32 connected to the first signal acquisition circuit 31 and the first communication unit 33 .

Specifically, the first signal acquisition circuit 31 includes an electrode interface 311 , a second first low-pass filter circuit 51412 , and a second analog-to-digital converter 313 . Wherein, the electrode interface 11 is in contact with the body surface of the human body to obtain physiological signals, and the second analog-to-digital converter 313 inputs the collected body surface physiological signals to the second micro-control unit 32 . Preferably, the second analog-to-digital converter 313 adopts a physiological signal analog-to-digital converter, which is also composed of a 24bits high-resolution large dynamic range chip ADS1299, has 8 channels in total, and can simultaneously collect 8 physiological electrical signals. The second first low-pass filter circuit 51412 defines a frequency band and has an anti-aliasing effect. Further preferably, the first signal acquisition circuit 31 further includes a bias drive circuit 314 connected to the second analog-to-digital converter 313. The bias drive circuit 314 is used to remove common-mode interference signals and eliminate baseline drift, so as to improve signal acquisition accuracy.

The second microcontroller unit 32 includes a microcontroller, and the microcontroller adopts a CC3220SF chip. It should be noted that the second micro-control unit 32 is compatible with the first communication unit 33, and in the case of the same communication efficiency, the configuration of the first communication unit 33 can reduce the integration space of the second micro-control unit 32 . And, between the second micro control unit 32 and the second analog-to-digital converter 313 in the present embodiment, a faster SPI communication mode is used to directly read the signal data in the second analog-to-digital converter 313 to achieve higher real-time.

The first communication unit 33 is used to interact with the terminal device 20 . Specifically, the first communication unit 33 is communicatively connected with the device terminal 10 to transmit the physiological signal collected by the first signal acquisition circuit 31 received by the second micro-control unit 32 to the device terminal 10 after being processed by the second micro-control unit 32 . So that the input terminal of the first micro-control unit 517 receives the physiological signals transmitted by the device terminal 10 according to the plurality of wearable wireless physiological sensors 30 and processes the obtained second stimulation parameters.

The wearable wireless physiological sensor 30 also includes an electrostatic protection circuit 34, a first serial port circuit 35, a first synchronization circuit 36, a second storage unit 37, a second power supply voltage stabilization circuit 38, a battery charge and discharge management circuit 39 and a power supply (lithium battery ). Specifically, the electrostatic protection circuit 34 is mainly designed for the external interface, to prevent the protection internal circuit from high voltage; the serial port circuit 35 is used for external debugging; the synchronization circuit 36 is used for data synchronization with other sensors; the second storage unit 37 is used for The physiological signal data collected by the wearable wireless physiological sensor 30 is stored locally, and the second storage unit 37 preferably adopts a TF card. The second power supply voltage regulator circuit 38 can generate +3.3V, +2.5V, -2.5V and other power rails, and the battery charge and discharge management circuit 39 can be used for energy saving and power consumption management to prolong battery life.

As shown in Figure 11 is a structural block diagram of the wearable wireless motion sensor 40, as shown in the figure, the wearable wireless motion sensor 40 includes a second signal acquisition circuit 41, a third micro control unit 42 connected to the second signal acquisition circuit 41 And the second communication unit 43, wherein the second signal acquisition circuit 41 includes a 9-axis motion sensor 411, and the 9-axis motion sensor is in contact with the body parts to acquire motion signals.

Specifically, the 9-axis motion sensor 411 uses a highly integrated chip MPU9250, and the 9-axis motion sensor 411 provides parameters such as acceleration, angular velocity, and magnetic force. Each 9-axis motion sensor 411 has a total of 3 AD acquisition outputs of 16-bit acceleration, 3 AD acquisition outputs of 16-bit gyroscope, and 3 AD acquisition outputs of 16-bit magnetometer, with slow speed and fast speed The range of the measurement range is programmable, such as the range of acceleration can be selected from ±2g, ±4g, ±8g, ±16g, and the gyroscope parameters can be selected from ±250d/s, ±500d/s, ±1000d/s, ±1000d/s, ± 2000d/s, the maximum range of the magnetometer can reach ±4800uT. And, in this embodiment, a faster SPI communication method is adopted between the third MCU 42 and the 9-axis motion sensor 411 to directly read the interrupt register data of the 9-axis motion sensor 411 to achieve higher real-time performance.

Of course, the wearable wireless motion sensor 40 also includes a power supply (lithium battery), a power supply voltage stabilization circuit, a battery charge and discharge management circuit, a storage unit, a serial port circuit, a synchronization circuit, an electrostatic protection circuit, etc., which are not further limited in this embodiment. , the connection structure parameters of the wearable wireless motion sensor 40 are shown in FIG. 4 , and will not be repeated here.

It should be noted that, as shown in FIG. 11, the second communication unit 43 is communicatively connected with the equipment terminal 20, so that the motion signal collected by the second signal acquisition circuit 41 received by the third micro-control unit 42 is processed by the third micro-control unit 42. Then send it to the equipment terminal 20. The input terminal of the first micro-control unit 517 receives motion signals transmitted by the device terminal 20 according to the plurality of wearable wireless motion sensors 40 and processes the obtained third stimulation parameters.

Therefore, the input terminal of the control computing center 52 receives the first stimulation parameter, the second stimulation parameter and the third stimulation parameter, and fuses to obtain the target stimulation parameter and outputs it to the stimulation execution unit. In this embodiment, there is no limitation on the amount fusion algorithm adopted by the control computing center 52, which may be a logical AND operation, a logical OR operation, or any practicable custom algorithm.

Taking the second embodiment as an example, the closed-loop deep brain stimulation decision-making method based on the system will be further described in detail below.

When the system includes a wearable wireless physiological sensor and/or a wearable wireless motion sensor, and a local field potential physiological sensor, the system constitutes two or three closed-loop modes. The first closed-loop mode is based on the closed-loop stimulation formed by the wearable wireless sensory stimulator using local field potential information as feedback. The second closed-loop mode is based on the closed-loop stimulation formed by the wearable wireless physiological sensor and the body surface physiological signal as feedback. The third closed-loop mode is based on the closed-loop stimulation formed by the wearable wireless motion sensor using the motion signals of various parts of the body as feedback. It should be emphasized that the above three closed-loop modes are all controlled and stimulated by the control computing center and the stimulation execution unit in the wearable sensory stimulator to form a closed loop.

Of course, on the basis of adopting the first closed-loop mode, this embodiment adopts at least one of the second closed-loop mode and the third closed-loop mode at the same time, so that the stimulation data is more comprehensive in terms of types, more strategic, and closed-loop Stimulus control is more precise.

As shown in Figures 12 and 13, this embodiment further exemplifies the closed-loop deep brain stimulation decision-making method based on the above-mentioned three closed-loop modes as an example. For the unfinished description of related technical solutions, please refer to the above content.

Firstly, the measurement position of the above-mentioned wearable device sensor on the human body is selected and the device is worn. Exemplarily, FIG. 12 is a schematic diagram of the measurement position of the wearable wireless sensor in the human body of the present application, and the solid figure in the figure is marked as the conventional measurement position of the wearable sensor. Among them, 601 and 602 are implantable deep brain stimulation electrodes, which are connected to the wearable wireless sensory stimulator. The two electrodes are respectively implanted on the left and right sides of the human brain. Each electrode has 4 contacts and 2 contacts for differential recording. One stimulation point is stimulated, and the other stimulation point is left blank. The selection of stimulation contacts and recording contacts can be adjusted according to the position of implanted brain nuclei. This system uses implantable electrodes from Medtronic or PINS Medical.

The circular marks on the body surface shown in the figure indicate the approximate location of the wearable wireless physiological sensor, which is the measurement point of the physiological electrical signal. Among them, 621 is bound with a wireless physiological sensor to measure ECG signals, and 622, 623, 624 and 625 are bound with wireless physiological sensors to measure the EMG signals of the left and right forearm and forearm. 626, 627, 628 and 629 are bound with wireless physiological sensors to measure the electromyographic signals of the left and right thighs. For the measurement of the left and right arm and left and right leg EMG signals, the sensor binding position can be changed at any time according to the measured muscle group. Generally, the 2-channel differential recording is used as a group to measure the EMG signals of the muscle groups. Of course, the number of wearable wireless physiological sensors can be reduced or increased according to requirements, which is not limited in this embodiment. Since the wireless physiological signal sensor has 8-channel differential measurement, and according to the principle of convenience, that is, the EMG test of the left upper and lower limbs, the EMG test of the right upper and lower limbs, and the ECG measurement of the chest, at least 3 wearable wireless sensors are required. Physiological sensors allow accurate calculation of physiological state assessments based on electrophysiological information.

The square marks on the body surface shown in the figure all indicate the approximate location of the wearable wireless motion sensor, which is the measurement point of the motion signal. 611 and 612 are wireless motion sensors placed on the neck, used to measure the rotation angle and swing range on both sides of the neck, 617 and 618 are wireless motion sensors placed on the waist, used to measure the rotation angle and swing range of the waist, 613 and 614 are wireless motion sensors placed on the wrist for measuring the rotation angle and swing range of the wrist; 615 and 616 are wireless motion sensors placed at the ankles for measuring the rotation angle and swing range of the ankle. Of course, the number of wearable wireless motion sensors can be reduced or increased according to requirements, which is not limited in this embodiment. In order to accurately measure the motion information of the subjects, at least 5 motion sensors are required to accurately calculate the motion state assessment based on motion information.

After the target object wears and powers on a wearable device, in the specific implementation process, as shown in Figure 12, the closed-loop deep brain stimulation decision-making method includes the following steps:

S10, parameter initialization of the wearable wireless sensory stimulator, the wearable wireless physiological sensor, and the wearable wireless motion sensor.

S20. The wearable wireless sensory stimulator acquires the brain tissue impedance of the target object.

S30. The local field potential physiological sensor, the wearable wireless physiological sensor and the wearable wireless motion sensor (hereinafter referred to as each sensor) configure system parameters based on the impedance of the brain tissue of the target object, and establish a communication connection with the host computer. The system parameters include the acquisition parameters of each part of the sensor when collecting signals, and the preset stimulation parameters for the final brain stimulation, that is, the amplitude, pulse width, frequency, delay time, waveform type, etc. of the stimulation voltage or current. Data storage parameters, voltage safety threshold and current safety threshold used for subsequent stimulus monitoring.

S40. Each sensor collects a piece of data for self-calibration and/or baseline elimination.

S50. Each sensor collects and stores corresponding physiological signal data.

Specifically, the local field potential physiological sensor collects intracranial local field potential signal data, the wearable wireless physiological sensor collects body surface physiological signal data, and the wearable wireless motion sensor collects limb movement signal data. The data collected in this step is used as the data basis for the brain stimulation at the corresponding moment.

In addition to storing data in the corresponding SD card or TF card, each sensor also stores the data in the host computer through wireless transmission, and obtains the corresponding first stimulation parameter, second stimulation parameter and third stimulation parameter.

S60. Preprocessing each sensor data, specifically: performing preprocessing on the obtained first stimulation parameter, second stimulation parameter, and third stimulation parameter to obtain corresponding target positive value data.

S70. Configuring the target reference threshold in real time, including:

Smooth the obtained target fusion data in the time domain;

When the current time window is the first time window, configure the target reference threshold as a preset experience threshold;

When the current time window is any time window other than the first time window, obtain all first stimulation parameters, second stimulation parameters and third stimulation parameters corresponding to each time window before the current time window At least one window average value of the target positive value data; arrange at least one window average value in order and take the median value as the target reference threshold of the current time window.

S80. Fuse the three kinds of sensor data and output the target fusion data, specifically: perform logical AND operation or logical OR operation on the stimulation parameters corresponding to the acquired target object in the current time window and the three kinds of physiological signal data to achieve data fusion and obtain corresponding The target fusion data.

After step S60 is executed, the above steps S70 and S80 are executed synchronously.

S90. Determine whether the target fusion data is greater than the target reference threshold corresponding to the current time window. If yes, determine that deep brain stimulation is required, such as performing step S100. If not, determine that deep brain stimulation is not required, and perform step S110.

S100. Applying electrical stimulation with preset stimulation parameters to the target object through the implantable deep brain stimulation electrodes, specifically including: the calculation control center outputs a stimulation on signal to the stimulation execution unit, and the implantable deep brain stimulation electrodes follow the steps in step S30. The preset stimulation parameters were used for electrical stimulation. During the electrical stimulation process, step S120 is executed synchronously.

S110. Maintaining the non-stimulation state of the implantable deep brain stimulation electrode, specifically includes: the computing control center outputs a stimulation off signal to the stimulation execution unit to maintain the non-stimulation.

It should be noted that the purpose of the closed-loop deep brain stimulation decision-making method and system in this embodiment is to provide more accurate decision-making basis for accurate closed-loop brain stimulation, rather than to perform closed-loop deep brain stimulation. As far as the method is concerned, it is a simple data processing method, and it is not directly aimed at obtaining disease diagnosis results or health status, nor does it make technical improvements to the deep brain stimulation technology that acts on the human or animal body, that is, the above steps S100 and S110 are only for illustration The description is not included in the technical solution of this application.

S120. Perform real-time monitoring of the electrical stimulation performed, and monitor the brain stimulation current value corresponding to the output electrical stimulation. When the brain stimulation exceeds the preset current threshold, stop the implanted deep brain stimulation electrode from stimulating the target. Subject applies electrical stimulation.

S130. When the brain stimulation is completed or a sudden system failure occurs, the stimulation can be ended manually by receiving manual intervention, the implanted deep brain stimulation electrodes stop electrical stimulation, each sensor closes the storage file and stops communication, and ends.

As shown in Figure 14, this embodiment also provides a closed-loop deep brain stimulation decision-making device, which includes:

In a preferred embodiment, the device further includes a first configuration module, configured to configure system parameters and perform system calibration, including:

a first acquisition unit, configured to acquire the brain tissue impedance of the target object;

A first configuration unit, configured to adjust initialization parameters at system startup based on the brain tissue impedance to obtain initial parameters of the current system and perform system configuration;

The calibration unit is used for self-calibrating the system and/or removing baseline drift after the acquisition is started.

The plurality of physiological signal data includes intracranial local field potential signal data, body surface physiological signal data and limb movement signal data. Fusion modules include:

The second acquisition unit is configured to acquire the first stimulation parameter corresponding to the intracranial local field potential signal, the second stimulation parameter corresponding to the body surface physiological signal, and the third stimulation corresponding to the limb movement signal within the current time window parameter;

The first processing unit is configured to perform a logic AND operation or a logic OR operation on the first stimulation parameter, the second stimulation parameter and the third stimulation parameter to obtain target fusion data.

A preprocessing unit, configured to preprocess the obtained first stimulation parameter, the second stimulation parameter, and the third stimulation parameter; specifically for:

The device also includes: a second configuration module, configured to configure the target reference threshold in real time, including:

The second processing unit is used to smooth the obtained target fusion data in the time domain;

The third processing unit, when the current time window is the first time window: configure the target reference threshold as a preset experience threshold; when the current time window is any time window except the first time window: Obtain at least one window average value of target positive value data corresponding to all the first stimulation parameters, the second stimulation parameters, and the third stimulation parameters in each time window before the current time window; A windowed average is sorted and the median is taken as the target reference threshold for the current time window.

The device also includes: a monitoring module for real-time monitoring of the electrical stimulation performed, including:

The monitoring unit is used to monitor the brain current value corresponding to the output electrical stimulation;

The control unit is configured to stop applying electrical stimulation to the target object through the implanted deep brain stimulation electrodes when the brain current exceeds a preset current threshold.

It should be noted that when the closed-loop deep brain stimulation decision-making device provided by the above-mentioned embodiments triggers the closed-loop deep brain stimulation decision-making business, it only uses the division of the above-mentioned functional modules as an example. In practical applications, the above-mentioned functions can be allocated according to needs It is completed by different functional modules, that is, the internal structure of the system is divided into different functional modules to complete all or part of the functions described above. In addition, the closed-loop deep brain stimulation decision-making device and the embodiment of the closed-loop deep brain stimulation decision-making method provided by the above-mentioned embodiments belong to the same concept, that is, the system is based on this method, and its specific implementation process is detailed in the method embodiment, and will not be repeated here. .

In addition, this embodiment also provides an electronic device, including:

one or more processors; and

A memory associated with the one or more processors, the memory is used to store program instructions, and when the program instructions are read and executed by the one or more processors, execute the aforementioned closed-loop deep brain stimulation decision-making method .

As for the closed-loop deep brain stimulation decision-making method executed by executing the program instructions, the specific implementation details and corresponding beneficial effects are consistent with the descriptions of the aforementioned methods, and will not be repeated here.

All the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, that is, any number of embodiments can be combined to meet the needs of different application scenarios, all within the scope of protection of this application. This will not repeat them one by one.

It should be noted that the above descriptions are only preferred embodiments of the application, and are not intended to limit the application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the application shall include Within the protection scope of this application.

Claims

The closed-loop deep brain stimulation decision-making method is characterized in that the method comprises:

Corresponding stimulation parameters are respectively calculated based on multiple physiological signal data of the target object acquired in real time; the multiple physiological signal data include intracranial local field potential signals, and at least one of body surface physiological signals and limb movement signals;

For the acquired first stimulation parameter corresponding to the intracranial local field potential signal within the current time window of the target object, and the second stimulation parameter corresponding to the body surface physiological signal and/or related to the limb movement performing data fusion on the third stimulus parameter corresponding to the signal and obtaining corresponding target fusion data;

It is judged whether the target fusion data is greater than the target reference threshold corresponding to the current time window, and if so, deep brain stimulation is required.
The method according to claim 1, wherein before the real-time acquisition of multiple physiological signal data of the target object, the method further includes configuring system parameters and performing system calibration, including:

Obtaining the brain tissue impedance of the target object;

Adjusting initialization parameters at system startup based on the brain tissue impedance to obtain initial parameters of the current system and performing system configuration;

After starting acquisition, self-calibrate the system and/or remove baseline drift.
The method according to claim 1, wherein the plurality of physiological signal data includes intracranial local field potential signal data, body surface physiological signal data and limb movement signal data;

The pair of acquired first stimulation parameters corresponding to the intracranial local field potential signal of the target object within the current time window, and second stimulation parameters corresponding to the body surface physiological signal and/or related to the Perform data fusion on the third stimulation parameters corresponding to the limb movement signals and obtain corresponding target fusion data, including:

Acquire the first stimulation parameter corresponding to the intracranial local field potential signal, the second stimulation parameter corresponding to the body surface physiological signal, and the third stimulation parameter corresponding to the limb movement signal in the current time window;

Perform a logical AND operation or a logical OR operation on the first stimulation parameter, the second stimulation parameter and the third stimulation parameter to obtain target fusion data.
The method according to claim 3, characterized in that, before performing a logical AND operation or a logical OR operation on the first stimulation parameter, the second stimulation parameter and the third stimulation parameter to obtain the target fusion data , performing data fusion on the acquired stimulation parameters corresponding to the plurality of physiological signal data of the target object within the current time window and obtaining corresponding target fusion data, further comprising: performing the data fusion on the obtained first stimulation parameters , the second stimulation parameter and the third stimulation parameter are preprocessed, including:

The first stimulation parameter, the second stimulation parameter and the third stimulation parameter are positively valued respectively to obtain target positive value data, and the positive value includes obtaining the time domain amplitude in the time domain and calculating the absolute value or frequency domain to do short-time Fourier to obtain the frequency domain amplitude.
The method according to claim 4, wherein before the judging whether the target fusion data is greater than the target reference threshold corresponding to the current time window, the method further comprises: configuring the target reference threshold in real time, including:

smoothing the obtained target fusion data in the time domain;

When the current time window is the first time window, configuring the target reference threshold as a preset experience threshold;

When the current time window is any time window except the first time window, all the first stimulation parameters, the second stimulation parameters and the At least one window average value of the target positive value data corresponding to the third stimulation parameter; arrange the at least one window average value in order and take the median value as the target reference threshold of the current time window.
The method according to any one of claims 1-5, characterized in that the method also includes real-time monitoring of the target object, specifically including:

monitoring the brain current value output by the target object when performing deep brain stimulation;

When the brain current value exceeds the preset current threshold, it is determined that the deep brain stimulation needs to be stopped.
The closed-loop deep brain stimulation decision-making system is characterized in that the system includes implantable deep brain stimulation electrodes, terminal equipment, multiple wearable wireless physiological sensors and/or multiple wearable wireless motion sensors, and the implantable A wearable wireless sensory stimulator connected to a deep brain stimulation electrode; the local field potential physiological sensor includes a first micro-control unit;

The wearable wireless perceptual stimulator includes a local field potential physiological sensor, a control computing center, and a stimulation execution unit connected in sequence, and the local field potential physiological sensor, the stimulation execution unit are respectively connected with the implanted deep brain stimulation electrode connected to form a closed loop circuit;

The control computing center is based on the first stimulation parameter corresponding to the intracranial local field potential signal sent by the first micro-control unit, and the second stimulation parameter corresponding to the physiological signals of the plurality of wearable wireless physiological sensors and/or Perform data fusion on the third stimulation parameters corresponding to the motion signals of the plurality of wearable wireless motion sensors to obtain corresponding target fusion data, and determine whether the target fusion data is greater than the target reference threshold corresponding to the current time window, and if so, perform deep brain stimulation.
The system according to claim 7, wherein the local field potential physiological sensor further comprises a recording channel switch, a stimulation artifact suppression circuit and a first analog-to-digital converter connected in sequence, and the first micro-control unit and the first analog-to-digital converter is connected;

The recording channel switch is connected to the implantable deep brain stimulation electrode;

The input terminal of the first micro-control unit receives the converted intracranial local field potential signal output by the first analog-to-digital converter and outputs it to the input terminal of the control computing center so that the control computing center processes and obtains corresponding first stimulus parameter.
The system according to claim 7, wherein the stimulation artifact suppression circuit comprises a preamplifier circuit, a high-pass filter circuit, a first low-pass filter circuit, and a post-amplifier circuit connected in sequence; wherein, the preamplifier Circuit, the input terminal is configured as a differential input, and the output terminal is configured as a single-ended output;

A high-pass filter circuit is connected to the preamplifier circuit, the input end is configured as a single-ended input, and the output end is configured as a single-ended output;

The first low-pass filter circuit is connected to the high-pass filter circuit, the input end is configured as single-ended input, and the output end is configured as single-ended output;

The post-amplification circuit is connected to the first low-pass filter circuit, the input end is configured as a single-ended input, and the output end is configured as a differential output.
The system according to claim 9, wherein the preamplifier circuit includes an instrumentation amplifier INA1, an adjustable gain resistor Rg1, and a ±5V DC stabilized voltage source, and the adjustable gain resistor Rg sets the preamplifier The gain of the amplifier circuit is 40dB or 60dB.
The system according to claim 9, wherein the high-pass filter circuit is configured as a three-stage 6-stage circuit, and each stage 2-stage circuit includes a capacitor Ch1, a resistor Rh1, a capacitor Ch2, a resistor Rh2, and an operational amplifier Oph1 And ±5V DC power supply, the output terminal of the preamplifier circuit is connected to the P terminal of the operational amplifier Oph1 through the capacitor Ch1 and the capacitor Ch2 in turn, one end of the resistor Rh1 is connected between the capacitor Ch1 and the capacitor Ch2, and the other end is connected to The N terminal of the operational amplifier Oph1, one terminal of the resistor Rh2 is connected between the capacitor Ch2 and the P terminal of the operational amplifier Oph1, and the other terminal is grounded.
The system according to claim 9, wherein the first low-pass filter circuit is configured as a five-stage 10-stage circuit, and each stage of a 2-stage circuit includes a capacitor Cl1, a resistor Rl1, a capacitor Cl2, and a resistor Rl2. Amplifier Opl1 and ±5V DC power supply, the output end of the high-pass filter circuit is connected to the P terminal of the operational amplifier Opl1 through the resistor Rl1 and the resistor Rl2 in turn, one end of the capacitor Cl1 is connected between the resistor Rl1 and the resistor Rl2, and the other end is connected to the operational amplifier. The N end of the amplifier Opl1, one end of the capacitor Cl2 is connected between the resistor Rl2 and the P end of the operational amplifier Opl1, and the other end is grounded.
The system according to claim 9, wherein the post-amplification circuit includes an adjustable gain resistor Rf1, an adjustable gain resistor Rf2, a fully differential amplifier INA2, and a ±2.5V DC stabilized voltage source, and the adjustable The gain resistor Rf1 is connected to the P input terminal and the P output terminal of the fully differential amplifier INA2, the adjustable gain resistor Rf2 is connected to the N input terminal and the N output terminal of the fully differential amplifier INA2, and the adjustable gain resistor Rg sets the gain of the preamplifier circuit to 0dB-20dB.
The system according to claim 13, wherein the post-amplification circuit further includes a resistor Rb1, a resistor Rb2, and a matching resistor Rt, and the output end of the first low-pass filter circuit is connected to a fully differential amplifier through a resistor Rb1 The P input terminal of INA2, the N input terminal of the fully differential amplifier INA2 are grounded through the resistor Rb2, and the matching resistor Rt is connected to the input terminal of the post-amplification circuit.
The system according to claim 14, wherein the post-amplification circuit further comprises an output reference capacitor Cc and an output differential capacitor Cd, the output reference capacitor Cc is connected to the Voc pin of the fully differential amplifier INA2, The output differential capacitor Cd is connected between the P output terminal and the N output terminal of the fully differential amplifier INA2.
The system according to claim 7 or 8, wherein the stimulation execution unit comprises a stimulation circuit, and the stimulation circuit includes a constant voltage stimulation circuit and a constant current stimulation circuit arranged in parallel.
The system according to claim 16, wherein the stimulation execution unit further comprises an impedance detection circuit, and the impedance detection circuit is arranged in parallel with the stimulation circuit.
The system according to claim 16, wherein the stimulation circuit further comprises a monitoring circuit for monitoring the stimulation current in the constant voltage stimulation circuit or the constant current stimulation circuit.
The system according to claim 7 or 8, wherein the wearable wireless physiological sensor comprises a first signal acquisition circuit, a second micro-control unit connected to the first signal acquisition circuit, and a first communication unit;

The first communication unit is communicatively connected with the device terminal, so that the physiological signal collected by the first signal acquisition circuit received by the second micro-control unit is processed by the second micro-control unit and transmitted to the device terminal.
The system according to claim 19, wherein the first signal acquisition circuit includes an electrode interface connected in sequence, a second low-pass filter circuit, and a second analog-to-digital converter, and the electrode interface is in contact with the human body surface to obtain physiological signals.
The system according to claim 7 or 8, wherein the wearable wireless motion sensor comprises a second signal acquisition circuit, a third micro control unit connected to the second signal acquisition circuit, and a second communication unit;

The second communication unit is communicatively connected with the equipment terminal, so that the motion signal collected by the second signal acquisition circuit received by the third micro-control unit is processed by the third micro-control unit and transmitted to the device terminal.
The system according to claim 21, wherein the second signal acquisition circuit comprises a 9-axis motion sensor, and the 9-axis motion sensor is in contact with a body part to acquire motion signals.
The closed-loop deep brain stimulation decision-making device is characterized in that the device includes:

The processing module is used to calculate corresponding stimulation parameters based on multiple physiological signal data of the target object acquired in real time; the multiple physiological signal data include intracranial local field potential signals, and also include body surface physiological signals and limb movement signals. at least one of

A fusion module, configured to acquire the first stimulation parameter corresponding to the intracranial local field potential signal within the current time window of the target object, and the second stimulation parameter corresponding to the body surface physiological signal and/or performing data fusion on the third stimulation parameter corresponding to the limb movement signal and obtaining corresponding target fusion data;

Judging module, used to judge whether the target fusion data is greater than the target reference threshold corresponding to the current time window, if so, deep brain stimulation is required.
An electronic device, characterized in that it comprises:

one or more processors; and

A memory associated with the one or more processors, the memory is used to store program instructions, and when the program instructions are read and executed by the one or more processors, perform any one of claims 1-6 method described in the item.