阅读说明：本技术 电生理信号采集系统 (Electrophysiological signal acquisition system ) 是由 陈玥 马伯志 吕长泉 郝红伟 李路明 于 2021-03-11 设计创作，主要内容包括：本公开涉及一种电生理信号采集系统,所述系统包括电生理信号采集装置及处理装置,电生理信号采集装置包括金属壳体,脉冲发生模块,用于产生脉冲信号；多个电极；信号采集模块,用于采集心电信号及采集脑电信号；信号处理模块,用于去除心电信号及脑电信号中的刺激伪迹和/或心电伪迹；信号分析模块,用于提取心电信号及脑电信号中的特征作为脉冲调整参数；闭环控制模块,用于根据脉冲调整参数对脉冲发生模块的脉冲发生参数进行调整；信号传输模块,用于实现信号传输。本公开实施例可以去除采集结果中的心电伪迹和/或刺激伪迹,从而得到准确的脑深部电信号采集结果,复杂度低,且性能可靠、成本较低,适宜于推广应用。(The present disclosure relates to an electrophysiological signal acquisition system, comprising an electrophysiological signal acquisition device and a processing device, the electrophysiological signal acquisition device comprises a metal housing, a pulse generation module for generating a pulse signal; a plurality of electrodes; the signal acquisition module is used for acquiring electrocardiosignals and electroencephalogram signals; the signal processing module is used for removing the electrocardio signals and the stimulation artifacts and/or the electrocardio artifacts in the electroencephalogram signals; the signal analysis module is used for extracting characteristics in the electrocardiosignals and the electroencephalogram signals as pulse adjustment parameters; the closed-loop control module is used for adjusting the pulse generation parameters of the pulse generation module according to the pulse adjustment parameters; and the signal transmission module is used for realizing signal transmission. The method and the device can remove the electrocardio artifact and/or the stimulation artifact in the acquisition result so as to obtain an accurate brain deep electric signal acquisition result, and are low in complexity, reliable in performance, low in cost and suitable for popularization and application.)

1. An electrophysiological signal acquisition system, comprising an electrophysiological signal acquisition device and a processing device, wherein the electrophysiological signal acquisition device is configured to be implanted in a body to acquire electrophysiological signals, and the processing device is configured on an extracorporeal platform, wherein the electrophysiological signal acquisition device comprises a metal housing, a plurality of electrodes disposed outside the metal housing, and a pulse generation module, a signal acquisition module, and a closed-loop control module disposed inside the metal housing, and further comprises a signal processing module, a signal analysis module, and a signal transmission module, wherein,

the pulse generation module is used for generating a pulse signal;

the plurality of electrodes are used for conducting the pulse signals and receiving the electrophysiological signals;

the signal acquisition module is used for acquiring electrocardiosignals through any electrode and the metal shell and synchronously acquiring electroencephalogram signals through any one or more pairs of electrodes;

the signal processing module is used for receiving the electrocardiosignals and the electroencephalogram signals and processing the electrocardiosignals and the electroencephalogram signals so as to remove stimulation artifacts and/or electrocardio artifacts in the electrocardiosignals and the electroencephalogram signals;

the signal analysis module is used for receiving the processed electrocardiosignals and the electroencephalogram signals, analyzing the processed electrocardiosignals and the electroencephalogram signals, and extracting characteristics in the electrocardiosignals and the electroencephalogram signals as pulse adjustment parameters;

the closed-loop control module is used for receiving the pulse adjustment parameters and adjusting the pulse generation parameters of the pulse generation module according to the pulse adjustment parameters;

the signal transmission module is used for realizing the signal transmission of each module in the electrophysiological signal acquisition device and the signal transmission of the electrophysiological signal acquisition device and the processing device.

2. The system of claim 1,

the electrophysiological signal acquisition device and the processing device both comprise the signal transmission module,

the signal processing module and the signal analysis module are arranged in the electrophysiological signal acquisition device and/or in the processing device.

3. The system of claim 1, wherein the signal acquisition module comprises a monopolar acquisition channel unit and a bipolar acquisition channel unit, the monopolar acquisition channel unit is configured to acquire the ecg signal, the bipolar acquisition channel unit is configured to acquire the eeg signal, and each of the monopolar acquisition channel unit and the bipolar acquisition channel unit comprises:

the pre-processing module comprises a filter circuit and an amplifying circuit and is used for filtering and amplifying the received electric signals;

and the differential sampling module is connected to the preprocessing module and is used for sampling the received electric signals.

4. The system of claim 1, wherein the signal processing module is configured to:

in the absence of stimulation artifacts as determined from the brain electrical signal,

preprocessing the electrocardiosignals and the electroencephalogram signals to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals, wherein the preprocessing comprises filtering and abnormal signal marking;

removing abnormal signals in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, and extracting the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof;

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal; or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio signal and the time-frequency domain characteristics of the target electroencephalogram signal, outputting the target electroencephalogram signal.

5. The system of claim 1, wherein the signal processing module is configured to:

in the case where it is determined from the brain electrical signal that a stimulation artifact is present,

preprocessing the electrocardiosignals and the electroencephalogram signals to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals, wherein the preprocessing comprises filtering and abnormal signal marking;

removing abnormal signals and stimulation artifacts in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, and performing signal extraction on the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof;

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal; or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio signal and the time-frequency domain characteristics of the target electroencephalogram signal, outputting the target electroencephalogram signal.

6. The system of claim 1, wherein the signal processing module is configured to:

in the case where it is determined from the brain electrical signal that a stimulation artifact is present,

preprocessing the electrocardiosignals and the electroencephalogram signals to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals, wherein the preprocessing comprises filtering and abnormal signal marking;

removing abnormal signals in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, removing stimulation artifacts in the preprocessed electrocardiosignals, and extracting the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof;

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal and removing the stimulation artifacts in the target electroencephalogram signal; or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio signal, removing the stimulation track of the target electroencephalogram signal.

7. The system of any one of claims 4-6, wherein the signal processing module is configured to:

comparing the time-frequency domain characteristics of the target electrocardiosignal with the time-frequency domain characteristics of the target electroencephalogram signal to determine the interference of the electrocardiosignal to the electroencephalogram signal,

when the interference does not exist or the interference intensity is smaller than the preset interference intensity, determining that no electrocardio artifact exists in the target electroencephalogram signal; or

And when the interference intensity is greater than the preset interference intensity, determining that the electrocardio artifact exists in the target electroencephalogram signal.

8. The system of claim 7, wherein the signal processing module is configured to:

carrying out normalization processing on the target electrocardiosignal and the target electroencephalogram signal;

performing signal extraction, time domain analysis and frequency domain analysis on the target electrocardiosignal and the target electroencephalogram signal obtained by normalization processing to obtain time-frequency domain characteristics of the target electrocardiosignal and the time-frequency domain characteristics of the target electroencephalogram signal;

and comparing the time-frequency domain characteristics of the target electrocardiosignal with the time-frequency domain characteristics of the target electroencephalogram signal to determine the interference of the electrocardiosignal on the electroencephalogram signal.

9. The system of claim 4 or 5, wherein the signal processing module is configured to:

determining a first position of a QRS complex in the target electrocardiosignal;

determining a second position of the electrocardio-artifact in the target electroencephalogram signal according to the first position;

estimating the electrocardio-artifacts in the target electroencephalogram signal by using a regression method at the second position, and removing the electrocardio-artifacts in the target electroencephalogram signal; or

Extracting the waveform of the electrocardio-artifact in the electroencephalogram signal at the second position, establishing an artifact waveform template by utilizing the waveform of the electrocardio-artifact, and removing the electrocardio-artifact in the target electroencephalogram signal through template subtraction; or

And at the second position, taking the target electrocardiosignal as a reference signal, and removing electrocardio artifacts in the target electroencephalogram signal by using a self-adaptive filter.

10. The system of claim 6, wherein the signal processing module is configured to:

determining a first position of a QRS complex in the target electrocardiosignal;

determining a second position of the electrocardio-artifact in the target electroencephalogram signal according to the first position;

extracting the waveform of the electrocardio-artifact in the electroencephalogram signal at the second position, filtering the waveform of the electrocardio-artifact, establishing an artifact waveform template by using the filtered waveform of the electrocardio-artifact, and removing the electrocardio-artifact in the target electroencephalogram signal through template subtraction; or

And at the second position, taking the target electrocardiosignal as a reference signal, and removing electrocardio artifacts in the target electroencephalogram signal by using a self-adaptive filter.

Technical Field

The disclosure relates to the technical field of medical equipment, in particular to an electrophysiological signal acquisition system.

Background

The deep brain stimulator is an implantable medical appliance, is mainly used for treating dyskinesia and certain drug refractory mental disorders, and has important clinical value in the field of brain function disease treatment. The brain deep stimulator can be used as an active and fully-implanted medical instrument which is directly contacted with the deep part of the brain, and can also be used as an implanted interface of neural activity. At present, the integration of deep brain stimulator and implantable electroencephalogram recording technology has become a new technology, and is an important tool for developing closed-loop neural regulation and control and implantable brain-computer interface research.

However, implantable electrophysiological signal acquisition techniques still face certain challenges. Due to the complexity of the conduction environment in the body, multiple interferers tend to be superimposed with different intensities in a weak target signal. Among the local field potentials collected by deep brain stimulators, Electrocardiographic (ECG) interference is one of the major noise sources. In studies using this acquisition technique, ECG interference has caused a significant loss of data. Researchers sometimes have to adjust the recording contact combination for ECG noise, affecting the accuracy of the recording location. Furthermore, the intensity of the ECG noise may gradually increase over time. The problems limit the implanted electroencephalogram acquisition technology to really move towards clinical application

The ECG noise mainly comes from the deficiency of the hardware design of the implanted electroencephalogram acquisition device. The ECG is a common mode signal for the differential channel formed by the electrode contacts of the deep brain nuclei. The intensity of the ECG noise remaining in the acquisition results depends on the Common Mode Rejection Ratio (CMRR) of the differential channels. Experimental results show that the CMRR can obviously inhibit ECG noise only when the CMRR reaches more than 60dB, which is very difficult for an implanted electroencephalogram acquisition device. On one hand, because the brain deep stimulator needs to work in a human body for a long time, the power consumption and the volume of the brain deep stimulator are very limited, and the difficulty in realizing high-precision differential channel matching is very high. On the other hand, once there is a slight body fluid leakage phenomenon, the impedance matching of the differential channel will be broken, resulting in a decrease in CMRR. Therefore, under the current state of the art, it is difficult to completely remove the ECG noise by hardware design.

On the other hand, the ECG noise in the implanted electroencephalogram acquisition result is difficult to be directly removed by the existing algorithm. The conventional ECG noise removal method usually needs to add additional synchronous ECG recording channels or channels requiring signals to be processed to a certain number, however, the implanted electroencephalogram acquisition system usually works independently, and the number of contacts is limited, so that the implementation condition of the above method is difficult to satisfy. Although some algorithms can perform blind source ECG noise removal on single-channel electroencephalogram data, the algorithms have high computational complexity and still have to be verified in performance.

Disclosure of Invention

In view of the above, the present disclosure provides an electrophysiological signal collection system, which includes an electrophysiological signal collection device and a processing device, wherein the electrophysiological signal collection device is configured to be implanted in a body to collect electrophysiological signals, and the processing device is disposed on an extracorporeal platform, wherein the electrophysiological signal collection device includes a metal housing, a plurality of electrodes disposed outside the metal housing, and a pulse generation module, a signal collection module, and a closed-loop control module disposed inside the metal housing, and the system further includes a signal processing module, a signal analysis module, and a signal transmission module, wherein,

the pulse generation module is used for generating a pulse signal;

the plurality of electrodes are used for conducting the pulse signals and receiving the electrophysiological signals;

the signal acquisition module is used for acquiring electrocardiosignals through any electrode and the metal shell and synchronously acquiring electroencephalogram signals through any one or more pairs of electrodes;

the signal processing module is used for receiving the electrocardiosignals and the electroencephalogram signals and processing the electrocardiosignals and the electroencephalogram signals so as to remove stimulation artifacts and/or electrocardio artifacts in the electrocardiosignals and the electroencephalogram signals;

the signal analysis module is used for receiving the processed electrocardiosignals and the electroencephalogram signals, analyzing the processed electrocardiosignals and the electroencephalogram signals, and extracting characteristics in the electrocardiosignals and the electroencephalogram signals as pulse adjustment parameters;

the closed-loop control module is used for receiving the pulse adjustment parameters and adjusting the pulse generation parameters of the pulse generation module according to the pulse adjustment parameters;

the signal transmission module is used for realizing the signal transmission of each module in the electrophysiological signal acquisition device and the signal transmission of the electrophysiological signal acquisition device and the processing device.

In one possible implementation manner, the electrophysiological signal acquisition device and the processing device each include the signal transmission module,

the signal processing module and the signal analysis module are arranged in the electrophysiological signal acquisition device and/or in the processing device.

In a possible implementation manner, the signal acquisition module includes a monopole acquisition channel unit and a bipolar acquisition channel unit, the monopole acquisition channel unit is used for acquiring the ecg signal, the bipolar acquisition channel unit is used for acquiring the eeg signal, wherein the monopole acquisition channel unit and the bipolar acquisition channel unit both include:

the pre-processing module comprises a filter circuit and an amplifying circuit and is used for filtering and amplifying the received electric signals;

and the differential sampling module is connected to the preprocessing module and is used for sampling the received electric signals.

In one possible implementation, the signal processing module is configured to:

in the absence of stimulation artifacts as determined from the brain electrical signal,

preprocessing the electrocardiosignals and the electroencephalogram signals to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals, wherein the preprocessing comprises filtering and abnormal signal marking;

removing abnormal signals in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, and extracting the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof;

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal; or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio signal and the time-frequency domain characteristics of the target electroencephalogram signal, outputting the target electroencephalogram signal.

In one possible implementation, the signal processing module is configured to:

in the case where it is determined from the brain electrical signal that a stimulation artifact is present,

preprocessing the electrocardiosignals and the electroencephalogram signals to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals, wherein the preprocessing comprises filtering and abnormal signal marking;

removing abnormal signals and stimulation artifacts in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, and performing signal extraction on the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof;

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal; or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio signal and the time-frequency domain characteristics of the target electroencephalogram signal, outputting the target electroencephalogram signal.

In one possible implementation, the signal processing module is configured to:

in the case where it is determined from the brain electrical signal that a stimulation artifact is present,

preprocessing the electrocardiosignals and the electroencephalogram signals to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals, wherein the preprocessing comprises filtering and abnormal signal marking;

removing abnormal signals in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, removing stimulation artifacts in the preprocessed electrocardiosignals, and extracting the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof;

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal and removing the stimulation artifacts in the target electroencephalogram signal; or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio signal, removing the stimulation track of the target electroencephalogram signal.

In one possible implementation, the signal processing module is configured to:

comparing the time-frequency domain characteristics of the target electrocardiosignal with the time-frequency domain characteristics of the target electroencephalogram signal to determine the interference of the electrocardiosignal to the electroencephalogram signal,

when the interference does not exist or the interference intensity is smaller than the preset interference intensity, determining that no electrocardio artifact exists in the target electroencephalogram signal; or

And when the interference intensity is greater than the preset interference intensity, determining that the electrocardio artifact exists in the target electroencephalogram signal.

In one possible implementation, the signal processing module is configured to:

carrying out normalization processing on the target electrocardiosignal and the target electroencephalogram signal;

performing signal extraction, time domain analysis and frequency domain analysis on the target electrocardiosignal and the target electroencephalogram signal obtained by normalization processing to obtain time-frequency domain characteristics of the target electrocardiosignal and the time-frequency domain characteristics of the target electroencephalogram signal;

and comparing the time-frequency domain characteristics of the target electrocardiosignal with the time-frequency domain characteristics of the target electroencephalogram signal to determine the interference of the electrocardiosignal on the electroencephalogram signal.

In one possible implementation, the signal processing module is configured to:

determining a first position of a QRS complex in the target electrocardiosignal;

determining a second position of the electrocardio-artifact in the target electroencephalogram signal according to the first position;

estimating the electrocardio-artifacts in the target electroencephalogram signal by using a regression method at the second position, and removing the electrocardio-artifacts in the target electroencephalogram signal; or

Extracting the waveform of the electrocardio-artifact in the electroencephalogram signal at the second position, establishing an artifact waveform template by utilizing the waveform of the electrocardio-artifact, and removing the electrocardio-artifact in the target electroencephalogram signal through template subtraction; or

And at the second position, taking the target electrocardiosignal as a reference signal, and removing electrocardio artifacts in the target electroencephalogram signal by using a self-adaptive filter.

In one possible implementation, the signal processing module is configured to:

determining a first position of a QRS complex in the target electrocardiosignal;

determining a second position of the electrocardio-artifact in the target electroencephalogram signal according to the first position;

extracting the waveform of the electrocardio-artifact in the electroencephalogram signal at the second position, filtering the waveform of the electrocardio-artifact, establishing an artifact waveform template by using the filtered waveform of the electrocardio-artifact, and removing the electrocardio-artifact in the target electroencephalogram signal through template subtraction; or

And at the second position, taking the target electrocardiosignal as a reference signal, and removing electrocardio artifacts in the target electroencephalogram signal by using a self-adaptive filter.

Through the electrophysiological signal acquisition system, the embodiment of the disclosure synchronously acquires the electrocardiosignals and the electroencephalogram signals through the signal acquisition module, and removes electrocardio artifacts and/or stimulation artifacts in the acquisition results, so as to obtain accurate deep brain electrical signal acquisition results.

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

Fig. 1, 2 show block diagrams of an electrophysiological signal acquisition system according to an embodiment of the present disclosure.

Fig. 3 shows a schematic diagram of a signal acquisition module according to an embodiment of the present disclosure.

Fig. 4 shows an operation mode diagram of a signal processing module according to an embodiment of the present disclosure.

Fig. 5 shows a schematic diagram of a first operation mode of a signal processing module according to an embodiment of the present disclosure.

Fig. 6 shows a schematic diagram of a second operation mode of the signal processing module according to an embodiment of the disclosure.

Fig. 7 shows a schematic diagram of a second operation mode of the signal processing module according to an embodiment of the disclosure.

FIG. 8 shows a schematic diagram of cardiac electrical artifact processing by the signal processing module according to an embodiment of the present disclosure.

FIG. 9 is a schematic diagram illustrating cardiac artifact determination by a signal processing module according to an embodiment of the disclosure.

Fig. 10 is a schematic diagram illustrating cardiac artifact removal in a first mode and a second mode of operation of a signal processing module according to an embodiment of the disclosure.

FIG. 11 is a schematic diagram illustrating cardiac artifact removal for a third mode of operation of the signal processing module according to an embodiment of the present disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

Referring to fig. 1 and 2, fig. 1 and 2 show a block diagram of an electrophysiological signal acquisition system according to an embodiment of the disclosure.

As shown in fig. 1 and fig. 2, the system comprises an electrophysiological signal collection device and a processing device, wherein the electrophysiological signal collection device is implanted in a body to collect electrophysiological signals, the processing device is disposed on an extracorporeal platform, wherein the electrophysiological signal collection device comprises a metal housing 8, a plurality of electrodes 1 disposed outside the metal housing 8, and a pulse generation module 2, a signal collection module 3, and a closed-loop control module 6 disposed inside the metal housing 8, the system further comprises a signal processing module 4, a signal analysis module 5, and a signal transmission module 7, wherein,

the pulse generation module 2 is used for generating a pulse signal;

the plurality of electrodes 1 are used for conducting pulse signals, acting on target parts in a human body (such as the brain) and receiving the electrophysiological signals;

the signal acquisition module 3 is used for acquiring electrocardiosignals through any electrode 1 and the metal shell 8 and synchronously acquiring electroencephalogram signals through any one or more pairs of electrodes 1;

the signal processing module 4 is configured to receive the electrocardiographic signal and the electroencephalogram signal, and process the electrocardiographic signal and the electroencephalogram signal to remove stimulation artifacts and/or electrocardiographic artifacts in the electrocardiographic signal and the electroencephalogram signal;

the signal analysis module 5 is configured to receive the processed electrocardiographic signal and the processed electroencephalogram signal, analyze the processed electrocardiographic signal and the processed electroencephalogram signal, and extract features in the electrocardiographic signal and the electroencephalogram signal as pulse adjustment parameters;

the closed-loop control module 6 is configured to receive the pulse adjustment parameter, and adjust the pulse generation parameter of the pulse generation module 2 according to the pulse adjustment parameter;

the signal transmission module 7 is used for realizing the signal transmission of each module in the electrophysiological signal acquisition device and the signal transmission of the electrophysiological signal acquisition device and the processing device.

Through the electrophysiological signal acquisition system, the embodiment of the disclosure synchronously acquires the electrocardiosignals and the electroencephalogram signals through the signal acquisition module, and removes electrocardio artifacts and/or stimulation artifacts in the acquisition results, so as to obtain accurate deep brain electrical signal acquisition results.

In one example, the stimulation artifact may be an electrical signal that is directed, amplified by a stimulation pulse (e.g., emitted by a pulse generation module) that diffuses through the tissue organ or electrolyte solution inside and outside the body, and under the recording electrode.

In one example, the cardiac electrical artifact may be an electrical signal caused by electrical activity of the heart.

In a possible implementation manner, as shown in fig. 1 and fig. 2, the electrophysiological signal collection device and the processing device may each include the signal transmission module 7.

In one example, the signal transmission module may include a communication component (not shown in fig. 1, 2) that may be configured to facilitate wired or wireless communication within the system between various devices and between various modules of various devices or with other devices. The system may access a wireless network based on a communication standard, such as bluetooth, radio frequency communication, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short-range communications.

In one possible implementation manner, as shown in fig. 1 and fig. 2, the signal processing module 4 and the signal analyzing module 5 may be disposed in the electrophysiological signal collection device and/or in the processing device.

In one example, the system may have a first system operating mode and a second system operating mode.

In one example, as shown in fig. 1, in the first system operation mode, the signal processing module 4 and the signal analysis module 5 belong to an implanted portion of an implanted electrophysiological signal acquisition device, the signal acquisition module 3 transmits an acquisition result to the signal processing module 4, the signal processing module 4 removes stimulation artifacts and ECG artifacts to extract a pure electrophysiological signal, the processed signal can be transmitted to the signal analysis module 5 and the closed-loop control module 6 to implement closed-loop neural stimulation therapy, and the processed signal can also be wirelessly transmitted to an extracorporeal platform through the signal transmission module 7.

In one example, as shown in fig. 2, in the second system operating mode, the signal processing module 4 and the signal analysis module 5 belong to an external part of an implantable electrophysiological signal acquisition device, the signal acquisition module 4 transmits an acquisition result to the signal transmission module 7, the signal transmission module 7 wirelessly transmits data to the external signal processing module 4, the signal processing module 4 processes the data to remove stimulation artifacts and ECG artifacts, and extract a pure electrophysiological signal; the processed electrophysiological signals can be used for feature analysis by the signal analysis module 5.

In one example, the signal analysis module 5 and the signal processing module 4 may be implemented by processing components including, but not limited to, a single processor, or discrete components, or a combination of a processor and discrete components. The processor may comprise a controller having functionality to execute instructions in an electronic device, which may be implemented in any suitable manner, e.g., by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components. Within the processor, the executable instructions may be executed by hardware circuits such as logic gates, switches, Application Specific Integrated Circuits (ASICs), programmable logic controllers, and embedded microcontrollers.

In one example, the pulse generating module 2 may include a pulse generator, and the pulse generator may include a pulse generating circuit, and is configured to generate a pulse signal with a specific pulse generating parameter (e.g., pulse amplitude, width, frequency, duration, etc.), where the pulse signal may be a constant voltage signal or a constant current signal, and the pulse signal generated by the pulse generating module 2 may be applied to a target portion of a brain (which may be a human body or other animal body) through the electrode 1, so as to achieve an electrical stimulation therapeutic effect on a therapeutic target.

The embodiment of the present disclosure does not limit the specific implementation manner of the pulse generating module 2, and those skilled in the art can implement the pulse generating module according to the related art.

Referring to fig. 3, fig. 3 is a schematic diagram of a signal acquisition module according to an embodiment of the disclosure.

In a possible implementation manner, as shown in fig. 3, the signal acquisition module 3 may include a unipolar acquisition channel unit and a bipolar acquisition channel unit, where the unipolar acquisition channel unit is configured to acquire the electrocardiographic signal (through a metal shell and any one electrode contact), and the bipolar acquisition channel unit is configured to acquire the electroencephalogram signal (through any two electrode contacts), where the unipolar acquisition channel unit and the bipolar acquisition channel unit may both include:

the pre-processing module 311 includes a filtering circuit and an amplifying circuit, and is configured to filter and amplify the received electrical signal;

and the differential sampling module 33 is connected to the pre-processing module 311 and is configured to sample the received electrical signal.

The embodiment of the present disclosure does not limit the specific implementation manner of the pre-amplification module 311, and a person skilled in the art may use a filter circuit (an RC filter circuit, etc.), an amplification circuit, or other circuits in the related art to form a pre-processing module to process the received electrical signal.

In an example, the differential sampling module may include an analog-to-digital converter ADC, and the embodiment of the present disclosure does not limit a specific implementation manner of the differential sampling module, and does not limit a specific process of differential sampling, which can be implemented by a person skilled in the art according to the related art.

The signal acquisition module 3 of the embodiment of the present disclosure can synchronously record ECG signals and brain electrical signals in deep brain. As shown in fig. 3, including a unipolar recording channel 31 and a bipolar recording channel 32; wherein the unipolar recording channel 31 is differentially recorded by the metal housing and any contact on the electrode for recording ECG signals; the bipolar acquisition channel 32 is differentially recorded by any two contacts on the electrodes and is used for recording electroencephalogram signals; unipolar and bipolar channel synchronous recording; in the unipolar recording channel 31, the voltages on the metal casing and the electrode contacts are processed by a pre-processing module 311; in the bipolar recording channel 32, the voltages on the electrode contacts are processed by a pre-processing 311 module; the voltage signals of the unipolar acquisition channel 31 and the bipolar acquisition channel 32 processed by the pre-processing module 311 are accessed to the differential sampling module 33, and are differentially amplified respectively to obtain two synchronous analog voltage signals, and the differential sampling module 33 converts the analog voltage signals of the unipolar acquisition channel 31 and the bipolar acquisition channel 32 into synchronous digital signals through synchronous sampling for subsequent processing.

Referring to fig. 4, fig. 4 is a schematic diagram illustrating an operation mode of a signal processing module according to an embodiment of the disclosure.

In one example, as shown in fig. 4, the signal processing module has a first operation mode, a second operation mode and a third operation mode; the signal processing module firstly judges whether an electroencephalogram signal obtained by a bipolar acquisition channel has stimulation artifacts, and if the electroencephalogram signal does not have the stimulation artifacts, the signal processing module enters a first working mode; if the stimulation artifact exists, the second or third operating mode is entered.

In one example, the method for judging whether the stimulation artifact exists by the signal processing module comprises the steps of obtaining the working state of the pulse generator, and carrying out time domain and frequency domain analysis on the electroencephalogram signals of the bipolar acquisition channels.

In one example, the stimulation artifact may be generated by a stimulation pulse, and the "presence or absence of stimulation artifact" shown in fig. 4 may be equivalent to whether the pulse generation module emits a pulse, in which case the presence or absence of stimulation artifact may be determined according to whether the pulse generation module emits a pulse; however, in some cases, such as 0V stimulation, or when one side of the bilateral neurostimulator is powered off and the other side is powered on, stimulation artifacts may appear in the acquisition result of the powered off side, and in these cases, time domain and frequency domain analysis may be performed on the electroencephalogram signals of the bipolar acquisition channel to determine whether the stimulation artifacts exist.

Referring to fig. 5, fig. 5 is a schematic diagram illustrating a first operation mode of a signal processing module according to an embodiment of the disclosure.

In one possible implementation, as shown in fig. 5, the signal processing module 4 is configured to:

in the absence of stimulation artifacts as determined from the brain electrical signal,

preprocessing the electrocardiosignals and the electroencephalogram signals (S41, S43) to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals, wherein the preprocessing comprises filtering (such as a Butterworth filter or other low-pass or band-pass IIR (infinite impulse response) or FIR (finite impulse response) filters and the like), abnormal signal marking (data such as discontinuity caused by data packet loss and abnormal fluctuation caused by motion artifacts can be called as abnormal data);

removing abnormal signals in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, and extracting the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof (S42);

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal (S44, S45); or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio signal and the time-frequency domain characteristics of the target electroencephalogram signal, outputting the target electroencephalogram signal.

In one example, as shown in fig. 5, the signal preprocessing step S41 is to preprocess the ecg signal output by the unipolar acquisition channel, where the preprocessing operations include filtering and abnormal signal marking; step S42, further analyzing the preprocessed single-channel acquisition result, eliminating abnormal signals, identifying the period of the ECG signal and the amplitude range of each component (such as Q wave, R wave and S wave) of the ECG by an analysis method such as a time domain and a frequency domain, determining the time domain and frequency domain characteristics of the ECG signal, and outputting a target ECG signal and the characteristics thereof; step S43, preprocessing the EEG signals of the bipolar acquisition channels, wherein the preprocessing operation comprises filtering and abnormal signal marking; step S44, comparing the characteristics of the target ECG signal output in the step S42 with the target electroencephalogram signal, analyzing the similarity of the target electroencephalogram signal and the target ECG signal in a time domain and a frequency domain, and judging whether an ECG artifact exists in the electroencephalogram signal; if the ECG artifact exists, removing the ECG artifact in the target electroencephalogram signal output in the step S43, and finally outputting the processed electroencephalogram signal.

Referring to fig. 6, fig. 6 is a schematic diagram illustrating a second operation mode of a signal processing module according to an embodiment of the disclosure.

In one possible implementation, as shown in fig. 6, the signal processing module 4 is configured to:

in the case where it is determined from the brain electrical signal that a stimulation artifact is present,

preprocessing the electrocardiosignals and the electroencephalogram signals (S51, S53) to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals, wherein the preprocessing comprises filtering and abnormal signal marking;

removing abnormal signals and stimulation artifacts in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, and extracting the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof (S52, S54);

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal (S55, S56); or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio signal and the time-frequency domain characteristics of the target electroencephalogram signal, outputting the target electroencephalogram signal.

In one example, as shown in fig. 6, the signal preprocessing step S51 preprocesses the ECG signal output by the unipolar acquisition channel, the preprocessing operations including filtering, abnormal signal labeling; step S52, further analyzing the processed single-channel acquisition result, eliminating abnormal signals, analyzing the signals in time domain and frequency domain, determining the frequency and intensity of the electrical stimulation artifact, removing the electrical stimulation artifact by using a high-pass filter, a low-pass filter or a self-adaptive filter, analyzing the time domain and frequency domain characteristics of the ECG signal of the removed signals, and outputting a target ECG signal and the time domain and frequency domain characteristics; step S53, preprocessing the EEG signals of the bipolar acquisition channels, wherein the preprocessing operation comprises filtering and abnormal signal marking; step S54, time domain and frequency domain analysis is carried out on the signal, the frequency and the intensity of the electrical stimulation artifact of the bipolar acquisition channel are determined, and the electrical stimulation artifact is removed by utilizing a high-pass filter, a low-pass filter or a self-adaptive filter; step S55, comparing the characteristics of the target ECG signal output in the step S52 with the target electroencephalogram signal, analyzing the similarity of the target electroencephalogram signal and the target ECG signal in a time domain and a frequency domain, and judging whether an ECG artifact exists in the electroencephalogram signal; step S56 identifies and removes ECG artifacts in the target electroencephalogram signal output in step S54.

Referring to fig. 7, fig. 7 is a schematic diagram illustrating a second operation mode of a signal processing module according to an embodiment of the disclosure.

In one possible implementation, as shown in fig. 7, the signal processing module 4 may be configured to:

in the case where it is determined from the brain electrical signal that a stimulation artifact is present,

preprocessing the electrocardiosignals and the electroencephalogram signals to obtain preprocessed electrocardiosignals and preprocessed electroencephalogram signals (S61, S63), wherein the preprocessing comprises filtering and abnormal signal marking;

removing abnormal signals in the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals, removing stimulation artifacts in the preprocessed electrocardiosignals, and extracting the preprocessed electrocardiosignals and the preprocessed electroencephalogram signals to obtain target electrocardiosignals and time-frequency domain characteristics thereof, and target electroencephalogram signals and time-frequency domain characteristics thereof (S62);

when the existence of the electrocardio-artifacts in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardio-signal and the time-frequency domain characteristics of the target electroencephalogram signal, removing the electrocardio-artifacts in the target electroencephalogram signal and removing the stimulation artifacts in the target electroencephalogram signal (S64, S65); or

And when the fact that no electrocardio artifact exists in the preprocessed electroencephalogram signal is determined according to the time-frequency domain characteristics of the target electrocardiosignal, removing the stimulation track of the target electroencephalogram signal (S66).

In one example, as shown in fig. 7, the signal preprocessing step S61 preprocesses the ECG signal output by the unipolar acquisition channel, the preprocessing operations including filtering, abnormal signal labeling; step S62, further analyzing the processed single-channel acquisition result, eliminating abnormal signals, analyzing the signals in time domain and frequency domain, determining the frequency and intensity of the electrical stimulation artifact, removing the electrical stimulation artifact by using a high-pass filter, a low-pass filter or a self-adaptive filter, analyzing the time domain and frequency domain characteristics of the ECG signal of the removed signals, and outputting a target ECG signal and the time domain and frequency domain characteristics thereof; step S63, preprocessing the EEG signals of the bipolar acquisition channels, wherein the preprocessing operation comprises filtering and abnormal signal marking; step S64, comparing the target ECG signal characteristics output in the step S62 with the target electroencephalogram signal, analyzing the similarity of the target ECG signal characteristics and the target electroencephalogram signal in time domain and frequency domain, and judging whether an ECG artifact exists in the target electroencephalogram signal; step S65 identifies and removes ECG artifacts in the bipolar acquisition result output in step S43; and step S66, performing time domain and frequency domain analysis on the signal, determining the frequency and intensity of the electrical stimulation artifact in the electroencephalogram signal of the bipolar acquisition channel, and removing the stimulation artifact by using a high-pass filter, a low-pass filter or a self-adaptive filter.

In one example, the third mode of operation differs from the second mode of operation in that the removal of stimulation artifact is put to a final step, such that in the third mode of operation, the disclosed embodiments do not rely on the removal of stimulation artifact when removing ECG artifact. In the second working mode, the stimulation artifact is removed first, and then the ECG artifact is removed, but in practical application, some residual stimulation artifacts cannot be avoided.

Referring to fig. 8, fig. 8 is a schematic diagram illustrating cardiac electrical artifact processing of a signal processing module according to an embodiment of the disclosure.

In one possible implementation, as shown in fig. 8, the signal processing module 4 is configured to:

comparing the time-frequency domain characteristics of the target electrocardiosignal with the time-frequency domain characteristics of the target electroencephalogram signal to determine the interference of the electrocardiosignal to the electroencephalogram signal,

when the interference does not exist or the interference intensity is smaller than the preset interference intensity, determining that no electrocardio artifact exists in the target electroencephalogram signal; or

And when the interference intensity is greater than the preset interference intensity, determining that the electrocardio artifact exists in the target electroencephalogram signal.

In one example, as shown in fig. 8, first, time domain and frequency domain analysis is performed on the electrocardiosignal and the electroencephalogram signal, time domain and frequency domain features of the signal are extracted, the time domain and frequency domain features of the electroencephalogram signal are compared with the time domain and frequency domain features of the ECG signal, whether a bipolar acquisition channel is interfered by the ECG signal or not is determined, and interference intensity is determined; if the ECG signal interference exists and the interference intensity is greater than the preset interference intensity, further removing the ECG artifact; if the ECG signal interference does not exist, or the interference intensity is far lower than the EEG signal intensity, the ECG artifact removal operation is not carried out any more, and the EEG signal is directly output.

The specific size of the preset interference strength is not limited in the embodiments of the present disclosure, and those skilled in the art can set the size according to actual situations or needs.

Referring to fig. 9, fig. 9 is a schematic diagram illustrating an electrocardiographic artifact determination of a signal processing module according to an embodiment of the present disclosure.

In one possible implementation, as shown in fig. 9, the signal processing module 4 is configured to:

carrying out normalization processing on the target electrocardiosignal and the target electroencephalogram signal;

performing signal extraction, time domain analysis and frequency domain analysis on the target electrocardiosignal and the target electroencephalogram signal obtained by normalization processing to obtain time-frequency domain characteristics of the target electrocardiosignal and the time-frequency domain characteristics of the target electroencephalogram signal;

and comparing the time-frequency domain characteristics of the target electrocardiosignal with the time-frequency domain characteristics of the target electroencephalogram signal to determine the interference of the electrocardiosignal on the electroencephalogram signal.

In one example, as shown in fig. 9, the ECG signal and the electroencephalogram signal are normalized separately, and the normalization processing method includes, but is not limited to, linear scaling, range transformation, zero-mean normalization, etc.; then, respectively carrying out time domain and frequency domain analysis on the ECG signal and the EEG signal; the time domain analysis method comprises the steps of calculating indexes of linear and nonlinear domains such as mean, standard deviation and sample entropy, and time domain correlation coefficients, mean square error, relative error and inner product of the ECG signal and the EEG signal; the frequency domain analysis method comprises but is not limited to calculating power spectral density, specific frequency band energy, and frequency domain correlation coefficient, frequency domain coherence, cross spectrum, frequency spectrum mean square error, frequency spectrum relative error and the like of the ECG signal and the EEG signal; and comparing and analyzing various indexes of a time domain and a frequency domain, and comprehensively judging whether the interference of the ECG artifact exists in the electroencephalogram signal.

In one example, in the ECG artifact determination, the number or the degree of similarity of the similarity indexes may be determined, and when the number or the degree of similarity of the similarity indexes reaches a predetermined standard, it may be determined that the ECG artifact interference exists in the electroencephalogram signal.

Referring to fig. 10, fig. 10 is a schematic diagram illustrating cardiac artifact removal in a first operation mode and a second operation mode of a signal processing module according to an embodiment of the disclosure.

In one possible implementation, the signal processing module 4 is configured to:

step S451, determining a first position of a QRS complex in the target electrocardiosignal;

step S452, determining a second position of the electrocardio artifact in the target electroencephalogram signal according to the first position;

step S4541, estimating the electrocardio-artifacts in the target electroencephalogram signal at the second position by using a regression method, and removing the electrocardio-artifacts in the target electroencephalogram signal; or

Step S453 and step S4592, extracting the waveform of the electrocardio artifact in the electroencephalogram signal at the second position, establishing an artifact waveform template by using the waveform of the electrocardio artifact, and removing the electrocardio artifact in the target electroencephalogram signal through template subtraction; or

And S4543, at the second position, taking the target electrocardiosignal as a reference signal, and removing the electrocardio artifact in the target electroencephalogram signal by using a self-adaptive filter.

In one example, the QRS complex may represent a composite complex of all ventricular muscle depolarization processes. A typical QRS complex comprises 3 closely-connected complexes, the 1 st downward wave, called the Q-wave or Q-wave; the 1 st upward wave is called R wave or R wave; the downward wave after the R wave is called S wave or S wave, and for the detailed description of the QRS complex, refer to the related art.

In one example, as shown in fig. 10, first identifying the location of each QRS wave of the ECG signal (S451), since the ECG signal (unipolar acquisition channel) is recorded synchronously with the electroencephalogram signal (bipolar acquisition channel), the location of the ECG artifact in the electroencephalogram signal can be determined from the location of the QRS wave in the ECG signal (S452); estimating ECG artifacts in the electroencephalogram signal by using a linear or nonlinear regression method, thereby removing the ECG artifacts (S4541); or after the position of the ECG artifact in the electroencephalogram signal is confirmed, extracting the waveform of the ECG artifact in the electroencephalogram acquisition result (S453), establishing a uniform or self-adaptive changing artifact waveform template by utilizing the artifact waveform, and removing the ECG artifact through template subtraction (S4542); the ECG signal may also be directly used as a reference signal, and the adaptive filter estimates ECG artifacts in the electroencephalogram signal to achieve the effect of removing the ECG artifacts (S4543), where the adaptive filter includes, but is not limited to, an adaptive filter based on Least Mean Square (LMS) algorithm, an adaptive filter based on Recursive Least Square (RLS) algorithm, a frequency domain adaptive filter, and the like.

Referring to fig. 11, fig. 11 is a schematic diagram illustrating an ecg artifact removal operation in a third operating mode of a signal processing module according to an embodiment of the disclosure.

In one possible implementation, the signal processing module 4 is configured to:

step S551, determining a first position of a QRS complex in the target electrocardiosignal;

step S552, determining a second position of the electrocardio artifact in the target electroencephalogram signal according to the first position;

s5531, S5532 and S5541, at the second position, extracting the waveform of the electrocardio artifact in the electroencephalogram signal, filtering the waveform of the electrocardio artifact, establishing an artifact waveform template by using the filtered waveform of the electrocardio artifact, and removing the electrocardio artifact in the target electroencephalogram signal through template subtraction; or

Step S5542, at the second position, taking the target electrocardiosignal as a reference signal, and removing electrocardio artifacts in the target electroencephalogram signal by using a self-adaptive filter.

In one example, as shown in fig. 11, first identifying the location of each QRS wave of the ECG signal (S551), determining the location of ECG artifacts in the brain electrical signal (S552) as the ECG signal (unipolar acquisition channel) is recorded synchronously with the brain electrical signal (bipolar acquisition channel); after the position of the ECG artifact in the electroencephalogram signal is confirmed, extracting the waveform of the ECG artifact in the electroencephalogram acquisition result (S5531), filtering the ECG artifact to remove the influence of the electrical stimulation artifact (S5532), wherein the filtering method comprises the steps of superposing average or filtering by a filter, thus establishing an artifact waveform template with uniform or self-adaptive change, and removing the ECG artifact through template subtraction (S5541); the ECG signal may also be directly used as a reference signal, and the adaptive filter estimates ECG artifacts in the electroencephalogram signal to achieve the effect of removing the ECG artifacts (S5542), where the adaptive filter includes, but is not limited to, an adaptive filter based on Least Mean Square (LMS) algorithm, an adaptive filter based on Recursive Least Square (RLS) algorithm, a frequency domain adaptive filter, and the like.

The embodiment of the disclosure can also analyze the processed electrocardiosignals and the electroencephalogram signals by using a signal analysis module, extract the characteristics of the electrocardiosignals and the electroencephalogram signals as pulse adjustment parameters, output the pulse adjustment parameters as feedback signals to a closed-loop control module, and adjust the pulse generation parameters (such as pulse amplitude, width, frequency, duration and the like) of the pulse generation module by using the pulse adjustment parameters through the closed-loop control module.

The embodiment of the disclosure does not limit the specific implementation manners of the signal analysis module and the closed-loop control module, does not limit the specific implementation manners of the analysis performed by the signal analysis module, and does not limit the types of the features (such as biomarkers) in the extracted electroencephalogram signal, and those skilled in the art can select and determine the features according to actual conditions or needs.

Compared with the prior art, the electrophysiological signal acquisition system can synchronously record a unipolar acquisition channel and a bipolar acquisition channel and extract a moderate ECG signal from the unipolar acquisition channel; the ECG signal is used as a reference to remove the ECG artifact, the embodiment of the invention can effectively identify whether the ECG artifact exists in the bipolar acquisition channel (namely, the electroencephalogram signal), and can accurately identify the position and the waveform of the ECG artifact in the electroencephalogram signal by using the reference signal, thereby effectively removing the ECG artifact; the embodiment of the disclosure has the reference channel, so that the removal of the ECG artifact is not easily influenced by the electrical stimulation artifact, and the removal effect of the ECG artifact is obviously improved.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.