CN116407140A

CN116407140A - Pulse signal detection method, device, equipment and storage medium

Info

Publication number: CN116407140A
Application number: CN202310272949.6A
Authority: CN
Inventors: 李文艺; 李骁健
Original assignee: Shenzhen Institute of Advanced Technology of CAS
Current assignee: Shenzhen Institute of Advanced Technology of CAS
Priority date: 2023-03-13
Filing date: 2023-03-13
Publication date: 2023-07-11

Abstract

The application relates to a pulse signal detection method, a pulse signal detection device, pulse signal detection equipment and a pulse signal storage medium. The method comprises the following steps: collecting multichannel nerve signals; pulse signal detection is carried out on the multichannel neural signals by adopting a double-threshold method, so as to obtain pulse signal time stamps of each channel; dividing the pulse signal time stamp into fixed time windows, counting whether pulse signals exist in each time window, and generating a binarization sequence corresponding to the pulse signals according to a counting result. According to the embodiment of the application, the pulse signals in the nerve signals are rapidly detected by adopting the double-threshold method, interference signals such as noise in the nerve signals can be eliminated, the efficiency and the accuracy of pulse signal detection are improved, the pulse detection can be directly carried out on the original nerve signals, and the method can be applied to online detection.

Description

Pulse signal detection method, device, equipment and storage medium

Technical Field

The application belongs to the technical field of nerve signal processing, and particularly relates to a pulse signal detection method, device, equipment and storage medium.

Background

Since the activity of neurons in the brain is mainly manifested in the generation, change and transmission of nerve electrical signals, recording and analysis of nerve electrical signals is a fundamental means of studying nerve activity. The nerve signal acquisition device has a very high sampling rate, so that a rapid signal detection algorithm needs to be developed to detect pulse signals in the acquired nerve signals in order to achieve the effect of real-time detection. At present, a commonly used pulse signal detection method comprises a template matching method and an amplitude threshold method, wherein the template matching method belongs to an off-line (offline) method, and is high in accuracy, but long in time consumption, difficult to apply to an on-line task and high in requirement on signal acquisition equipment. The amplitude threshold method is easy to detect noise as a pulse signal, so that the detection accuracy of the pulse signal is low.

Disclosure of Invention

The present application provides a pulse signal detection method, apparatus, device and storage medium, which aim to solve at least one of the above technical problems in the prior art to a certain extent.

In order to solve the above problems, the present application provides the following technical solutions:

a pulse signal detection method comprising:

collecting multichannel nerve signals;

pulse signal detection is carried out on the multichannel neural signals by adopting a double-threshold method, so as to obtain pulse signal time stamps of each channel;

dividing the pulse signal time stamp into fixed time windows, counting whether pulse signals exist in each time window, and generating a binarization sequence corresponding to the pulse signals according to a counting result.

The technical scheme adopted by the embodiment of the application further comprises: the pulse signal detection of the multichannel neural signal by adopting the double-threshold method comprises the following steps:

recording time information of the nerve signals with voltage amplitude larger than a set first threshold value for the nerve signals of each channel, and generating a first one-dimensional linked list of each channel about the first threshold value, wherein the first one-dimensional linked list comprises all threshold value sequences of the time information larger than the first threshold value;

recording time information of nerve signals with voltage amplitude larger than a set second threshold value, and generating a second one-dimensional linked list of each channel about the second threshold value, wherein the second one-dimensional linked list comprises all threshold value sequences of the time information larger than the second threshold value;

and comparing the first one-dimensional linked list with the second one-dimensional linked list, finding out and deleting the threshold sequence existing in the second one-dimensional linked list in the first one-dimensional linked list, and taking the remaining threshold sequence in the first one-dimensional linked list as a pulse sequence detection result.

The technical scheme adopted by the embodiment of the application further comprises: recording time information of the nerve signals with voltage amplitude larger than a set first threshold, wherein the generation of the first one-dimensional linked list of the multichannel nerve signals about the first threshold specifically comprises the following steps:

judging whether the voltage amplitude of the nerve signal in each channel is larger than a set first threshold value by adopting a first thread, if so, recording an initial timestamp and an end timestamp of the nerve signal in the channel, forming a section by the initial timestamp and the end timestamp, and storing the section into a first one-dimensional linked list.

The technical scheme adopted by the embodiment of the application further comprises: recording time information of the nerve signals with voltage amplitude larger than a set second threshold, wherein the generation of the second one-dimensional linked list of the multichannel nerve signals about the second threshold is specifically as follows:

and adopting a second thread to judge whether the voltage amplitude of the nerve signal in each channel is larger than a set second threshold value, if so, recording the initial timestamp and the end timestamp of the nerve signal in the channel, forming a section by the initial timestamp and the end timestamp, and storing the section into a second one-dimensional linked list.

The technical scheme adopted by the embodiment of the application further comprises: the step of comparing the first one-dimensional linked list and the second one-dimensional linked list, and the step of finding out the threshold sequence existing in the second one-dimensional linked list in the first one-dimensional linked list and deleting specifically comprises the following steps:

if the first threshold value and the nerve signal have two adjacent intersection points, a section formed by the two adjacent intersection points is marked as A, if the second threshold value and the nerve signal also have two adjacent intersection points, a section formed by the two intersection points is marked as B, whether the section B is a non-empty proper subset of the section A is judged, if so, the threshold sequence recorded by the section A is judged to be an interference signal, and the threshold sequence is deleted from the section A; otherwise, calculating the midpoint position of the interval A and enabling the midpoint position to be approximately equal to the pulse peak position, recording corresponding time information, and generating a pulse signal time stamp.

The technical scheme adopted by the embodiment of the application further comprises: after the remaining threshold sequences in the first one-dimensional linked list are used as the pulse sequence detection results, the method further comprises the following steps:

and (3) averaging the time information of all pulse sequences in the pulse sequence detection result to approximate to a pulse peak, and obtaining a pulse signal time stamp.

The technical scheme adopted by the embodiment of the application further comprises: after the binary sequence corresponding to the pulse signal is generated according to the statistical result, the method further comprises the following steps:

and inputting the binarized sequence into a neural network for classification.

The embodiment of the application adopts another technical scheme that: a pulse signal detection apparatus comprising:

the signal acquisition module: for acquiring multichannel neural signals;

and the signal detection module is used for: the method is used for detecting pulse signals of the multichannel neural signals by adopting a double-threshold method to obtain pulse signal time stamps of each channel;

and a binarization module: the method is used for dividing the pulse signal time stamp into fixed time windows, counting whether pulse signals exist in each time window, and generating a binarization sequence corresponding to the pulse signals according to a counting result.

The embodiment of the application adopts the following technical scheme: an apparatus comprising a processor, a memory coupled to the processor, wherein,

the memory stores program instructions for implementing the pulse signal detection method;

the processor is configured to execute the program instructions stored by the memory to control pulse signal detection.

The embodiment of the application adopts the following technical scheme: a storage medium storing program instructions executable by a processor for performing the pulse signal detection method.

Compared with the prior art, the beneficial effect that this application embodiment produced lies in: the pulse signal detection method, the device, the equipment and the storage medium of the embodiment of the application adopt a double-threshold method to rapidly detect the pulse signal in the nerve signal, can eliminate interference signals such as noise in the nerve signal, improve the efficiency and the accuracy of pulse signal detection, and convert the timestamp corresponding to the detected pulse signal into a binarization sequence, thereby being convenient for storage and subsequent task processing. The method and the device are simple to operate, can directly perform pulse detection on the original nerve signals, can be applied to online detection, and can be applied to pulse detection tasks of other signals such as pulse detection and heartbeat signal detection in biological signals.

Drawings

FIG. 1 is a flow chart of a pulse signal detection method according to an embodiment of the present application;

FIG. 2 is a flow chart of pulse detection using a dual threshold method in an embodiment of the present application;

FIG. 3 is a schematic diagram of single-channel nerve pulse signal detection based on a dual-threshold method according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a pulse signal detection device according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a pulse signal detection apparatus according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a storage medium according to an embodiment of the present application.

Detailed Description

The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

The terms "first," "second," "third," and the like in this application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", and "a third" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. All directional indications (such as up, down, left, right, front, back … …) in the embodiments of the present application are merely used to explain the relative positional relationship, movement, etc. between the components in a particular gesture (as shown in the drawings), and if the particular gesture changes, the directional indication changes accordingly. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

Referring to fig. 1, a flowchart of a pulse signal detection method according to an embodiment of the present application is shown. The pulse signal detection method of the embodiment of the application comprises the following steps:

s100: collecting multichannel nerve signals by using nerve electrode equipment;

in this step, the neural signal acquisition device such as the implantable utah electrode can acquire multichannel neural signals, the acquired neural signals are time sequence data with multichannel high sampling rate, the acquisition frequency of the neural signals is 20kHz or 30kHz, and the number of channels for acquiring the neural signals can be set according to the type of the neural electrode device, for example, 84 channels or 128 channels.

S110: pulse detection is carried out on the multichannel neural signals by adopting a double-threshold method, so as to obtain a pulse signal time stamp of each channel;

in this step, since the acquired neural signals are time-series data of a multi-channel high sampling rate, pulse detection is required for the neural signals in each channel. Specifically, as shown in fig. 2, a flow chart of pulse detection using a dual threshold method according to an embodiment of the present application specifically includes the following steps:

s111: judging whether the voltage amplitude of the nerve signal in each channel is larger than a set first threshold value by adopting a first thread, and if so, executing S112;

s112: recording the time information of the nerve signal in the channel, wherein the time information comprises an initial time stamp and an end time stamp, forming an interval by the initial time stamp and the end time stamp and storing the interval into a linked list, and generating a first one-dimensional linked list of the channel about a first threshold value;

in this step, the first one-dimensional linked list includes all the threshold sequences of the initial timestamp and the final timestamp that are greater than the first threshold, for example, the one-dimensional linked list of one channel may be represented as [ [ start1, end1], [ start2, end2], and.

S113: adopting a second thread to synchronously judge whether the voltage amplitude of the nerve signal in each channel is larger than a set second threshold value, and if so, executing S114;

s114: recording the time information of the nerve signal in the channel, forming an interval by the initial timestamp and the tail timestamp, storing the interval into a linked list, and generating a second one-dimensional linked list of the channel about a second threshold value;

in this step, the second one-dimensional linked list includes all the threshold sequences of the initial timestamp and the final timestamp greater than the second threshold, similarly to S112. According to the method and the device, the first thread and the second thread are adopted to judge the first threshold value and the second threshold value respectively, so that the effect of synchronous data processing is achieved, and the improvement of the pulse signal detection efficiency is facilitated.

S115: comparing the first one-dimensional linked list with the second one-dimensional linked list, judging whether a threshold sequence in the first one-dimensional linked list exists in the second one-dimensional linked list, and executing S116 if the threshold sequence exists;

s116: deleting the threshold sequence from the first one-dimensional linked list;

in this step, if a certain threshold sequence in the first one-dimensional linked list exists in the second one-dimensional linked list, which means that the detected pulse signal is too large at this time, and the pulse signal is determined to be noise, the corresponding threshold sequence is deleted in the first one-dimensional linked list.

S117: and taking the remaining threshold sequence in the first one-dimensional linked list as a pulse sequence detection result, and averaging the initial time stamps and the end time stamps of all the pulse sequences to approximate a pulse peak, so as to obtain a pulse signal time stamp [ timestamp1, timestamp2 ]. A. Timestamp N ].

S120: dividing the pulse signal time stamp into fixed time windows, counting whether pulse signals exist in each time window, and generating a binarization sequence corresponding to the pulse signals according to a counting result;

in the step, the pulse signal time stamp is subjected to binarization processing to obtain a binarization sequence which is more convenient to process and store, and convenience is provided for subsequent classification and other operations. Assuming that the sampling rate of the neural electrode device is 30kHz (i.e. 30000 data are collected in 1 second), if a channel is converted into a time window of 1msec (millisecond), the data of 30 time steps need to be divided into a time window, which can be specifically set according to the actual application scenario; the pulse signal time stamp is then mapped to a corresponding time window, i.e. k data can be obtained per second, each data corresponding to an interval of 1ms, so that the following binarized sequence can be obtained: [0,0,1,1,1,0..1 ], wherein 1 represents that there is a pulse signal in this time window and 0 represents that there is no pulse signal in this time window.

Specifically, as shown in fig. 3, a single-channel nerve pulse signal detection schematic diagram based on a dual-threshold method in the embodiment of the present application is shown. In fig. 3, taking a channel as an example, two horizontal lines in the figure represent a first threshold and a second threshold respectively, a curve represents a neural signal, when the voltage amplitude in the neural signal exceeds the first threshold, a peak value is found between the voltage amplitudes greater than the first threshold, and the horizontal coordinates (i.e. the moment information) corresponding to the peak value are reserved, so that a possible pulse signal is found. However, in the practical application scenario, some noise with a response amplitude exceeding the first threshold value may be mistaken as a pulse signal, so that the embodiment of the application further judges the found pulse signal by setting the second threshold value, specifically: if the first threshold value and the nerve signal have two adjacent intersection points, the interval formed by the two adjacent intersection points is marked as A, if the second threshold value and the nerve signal also have two adjacent intersection points, the interval formed by the two adjacent intersection points is marked as B, whether the B is a non-empty proper subset of the A is judged, if so, the signal recorded by the interval A is judged to be an interference signal such as noise, and the signal is deleted from the interval A; otherwise, directly and quickly calculating the midpoint position of the interval A and enabling the midpoint position to be approximately equal to the pulse peak position, recording corresponding time information, and finally generating a pulse signal time stamp. After all the pulse signal time stamps are obtained, dividing the whole pulse signal time stamp into fixed time windows, then counting whether pulse signals exist in each time window, if so, marking 1, otherwise, marking 0.

S130: and inputting the binarized sequences into a neural network for classification.

In the step, when the classification task is carried out, the binarized sequence can be directly input into a neural network for processing, so that the aim of rapid classification processing is fulfilled.

Based on the above, the pulse signal detection method of the second embodiment of the present application uses a dual-threshold method to rapidly detect the pulse signal in the neural signal, so that interference signals such as noise in the pulse signal can be eliminated, the efficiency and accuracy of pulse signal detection are improved, and the detected pulse signal timestamp is converted into a binarization sequence, so that storage and subsequent task processing are facilitated. The method and the device are simple to operate, can directly perform pulse detection on the original nerve signals, can be applied to online detection, and can be applied to pulse detection tasks of other signals such as pulse detection and heartbeat signal detection in biological signals.

Fig. 4 is a schematic structural diagram of a pulse signal detection device according to an embodiment of the present application. The pulse signal detection apparatus 40 of the embodiment of the present application includes:

signal acquisition module 41: for acquiring multichannel neural signals;

signal detection module 42: the method is used for detecting pulse signals of the multichannel neural signals by adopting a double-threshold method to obtain pulse signal time stamps of each channel;

binarization module 43: the method is used for dividing the pulse signal time stamp into fixed time windows, counting whether pulse signals exist in each time window, and generating a binarization sequence corresponding to the pulse signals according to a counting result.

Based on the above, the pulse signal detection device in the embodiment of the application adopts the dual-threshold method to rapidly detect the pulse signal in the nerve signal, so that interference signals such as noise in the nerve signal can be eliminated, the efficiency and accuracy of pulse signal detection are improved, and the timestamp corresponding to the detected pulse signal is converted into a binarization sequence, so that the pulse signal detection device is convenient to store and process subsequent tasks. The method and the device are simple to operate, can directly perform pulse detection on the original nerve signals, can be applied to online detection, and can be applied to pulse detection tasks of other signals such as pulse detection and heartbeat signal detection in biological signals.

Please refer to fig. 5, which is a schematic diagram of an apparatus structure according to an embodiment of the present application. The apparatus 50 comprises:

a memory 51 storing executable program instructions;

a processor 52 connected to the memory 51;

the processor 52 is configured to call the executable program instructions stored in the memory 51 and perform the steps of: collecting multichannel nerve signals; pulse signal detection is carried out on the multichannel neural signals by adopting a double-threshold method, so as to obtain pulse signal time stamps of each channel; dividing the pulse signal time stamp into fixed time windows, counting whether pulse signals exist in each time window, and generating a binarization sequence corresponding to the pulse signals according to a counting result.

The processor 52 may also be referred to as a CPU (Central Processing Unit ). The processor 52 may be an integrated circuit chip having signal processing capabilities. Processor 52 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an integrated circuit (ASIC) off-the-shelf programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

Referring to fig. 6, fig. 6 is a schematic structural diagram of a storage medium according to an embodiment of the present application. The storage medium of the embodiment of the present application stores program instructions 61 capable of implementing the steps of: collecting multichannel nerve signals; pulse signal detection is carried out on the multichannel neural signals by adopting a double-threshold method, so as to obtain pulse signal time stamps of each channel; dividing the pulse signal time stamp into fixed time windows, counting whether pulse signals exist in each time window, and generating a binarization sequence corresponding to the pulse signals according to a counting result. The program instructions 61 may be stored in the storage medium as a software product, and include several instructions to cause a device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program instructions, or a terminal device such as a computer, a server, a mobile phone, a tablet, or the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the partitioning of elements is merely a logical functional partitioning, and there may be additional partitioning in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not implemented. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. The foregoing is only the embodiments of the present application, and not the patent scope of the present application is limited by the foregoing description, but all equivalent structures or equivalent processes using the contents of the present application and the accompanying drawings, or directly or indirectly applied to other related technical fields, which are included in the patent protection scope of the present application.

Claims

1. A pulse signal detection method, comprising:

collecting multichannel nerve signals;

2. The pulse signal detection method according to claim 1, wherein the pulse signal detection of the multichannel neural signal using the double-threshold method comprises:

3. The pulse signal detection method according to claim 2, wherein the recording of the time information of the neural signal with the voltage amplitude greater than the set first threshold value, the generating of the first one-dimensional linked list of the multichannel neural signal with respect to the first threshold value is specifically:

4. The pulse signal detection method according to claim 3, wherein the recording of the time information of the neural signal with the voltage amplitude greater than the set second threshold value, the generating of the second one-dimensional linked list of the multichannel neural signal with respect to the second threshold value is specifically:

5. The method of claim 4, wherein comparing the first one-dimensional linked list with the second one-dimensional linked list, finding out a threshold sequence existing in the second one-dimensional linked list in the first one-dimensional linked list, and deleting the threshold sequence specifically comprises:

6. The method for detecting pulse signals according to claim 2, wherein after said taking the remaining threshold sequences in the first one-dimensional linked list as pulse sequence detection results, further comprises:

7. The method for detecting a pulse signal according to any one of claims 1 to 6, further comprising, after generating a binarization sequence corresponding to the pulse signal according to the statistical result:

and inputting the binarized sequence into a neural network for classification.

8. A pulse signal detection apparatus, comprising:

the signal acquisition module: for acquiring multichannel neural signals;

9. A pulse signal detection apparatus, characterized in that the apparatus comprises a processor, a memory coupled to the processor, wherein,

the memory stores program instructions for implementing the pulse signal detection method according to any one of claims 1 to 7;

10. A storage medium storing program instructions executable by a processor for performing the pulse signal detection method according to any one of claims 1 to 7.