CN114190940A

CN114190940A - Fatigue detection method and device, electronic equipment and storage medium

Info

Publication number: CN114190940A
Application number: CN202111396127.6A
Authority: CN
Inventors: 魏大雪; 孙铄
Original assignee: Lifeon Shenzhen Medical Technology Co ltd
Current assignee: Lifeon Shenzhen Medical Technology Co ltd
Priority date: 2021-11-23
Filing date: 2021-11-23
Publication date: 2022-03-18
Anticipated expiration: 2041-11-23
Also published as: CN114190940B

Abstract

The invention provides a fatigue detection method, a fatigue detection device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring initial electrocardiosignal data, and performing pre-filtering, differential amplification and analog-to-digital conversion on the initial electrocardiosignal data to obtain digital electrocardiosignal data; preprocessing the digital electrocardiosignal data and extracting characteristics to obtain RR interval sequence data; performing interval filtering, resampling and trend item removing on the RR interval series data to obtain RR interval histogram data; creating a regression model, and establishing a corresponding relation between the RR interval histogram data and a preset fatigue level based on the regression model; and acquiring electrocardiosignal data to be detected, and carrying out fatigue detection according to the corresponding relation. According to the invention, the electrocardiosignals of the human body are obtained and classified, the corresponding relation between the electrocardiosignals and the fatigue degree is determined, and the fatigue state can be accurately detected according to the corresponding relation.

Description

Fatigue detection method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of fatigue detection, in particular to a fatigue detection method and device, electronic equipment and a storage medium.

Background

Chronic diseases such as cardiovascular diseases, diabetes and cancer are characterized by late onset and unrecognizable early stage, and the early screening, early diagnosis and treatment of early stage risk factors or early stage diseases can delay or prevent the incidence and degree of the onset.

At present, mental fatigue is a key cause of many chronic diseases such as cardiovascular diseases, diabetes and cancer, but the mental fatigue is difficult to quantitatively evaluate and measure, so the detection of mental fatigue presents a severe test for the medical and nursing industries.

Disclosure of Invention

In view of the above, it is desirable to provide a fatigue detection method, apparatus, electronic device and storage medium, which can achieve accurate detection and identification of mental fatigue.

In order to solve the above technical problem, the present invention provides a fatigue detection method, including:

acquiring initial electrocardiosignal data, and performing pre-filtering, differential amplification and analog-to-digital conversion on the initial electrocardiosignal data to obtain digital electrocardiosignal data;

preprocessing the digital electrocardiosignal data and extracting characteristics to obtain RR interval sequence data;

performing interval filtering, resampling and trend item removing on the RR interval series data to obtain RR interval histogram data;

creating a regression model, and establishing a corresponding relation between the RR interval histogram data and a preset fatigue level based on the regression model;

and acquiring electrocardiosignal data to be detected, and carrying out fatigue detection according to the corresponding relation.

Preferably, the pre-filtering, differential amplifying and analog-to-digital converting the initial electrocardiograph signal data to obtain digital electrocardiograph signal data includes:

filtering out a high-frequency component signal with the frequency higher than the preset multiplying power of the sampling frequency by adopting a front low-pass filter;

carrying out differential amplification on the electrocardiosignal data processed by the pre-low-pass filter to obtain a differential amplification signal;

and performing analog-to-digital conversion on the differential amplification signal to obtain the digital electrocardiosignal data.

Preferably, the preprocessing the digital electrocardiograph signal data includes:

and sequentially carrying out digital high-pass filtering, digital notch filtering and digital low-pass filtering on the digital electrocardiosignal data to obtain preprocessed electrocardiosignal data.

Preferably, the obtaining of RR interval sequence data after feature extraction includes:

performing feature extraction on the preprocessed electrocardiosignal data based on QRS wave amplitude by adopting a digital band-pass filter;

judging whether the electrocardiosignal data after feature extraction exceeds a preset threshold value or not, and acquiring the RR interval sequence data when the electrocardiosignal data exceeds the preset threshold value.

Preferably, the RR interval histogram data obtained by performing interval filtering, resampling and trend term removal on the RR interval series data includes:

setting a sliding window to filter the RR interval sequence data;

resampling the RR interval sequence data to obtain RR interval sequence data with equal intervals;

performing trend item removing processing on the RR interval sequence data by adopting a digital high-pass filter;

and extracting the maximum value and the minimum value of the RR interval sequence after interval filtering, resampling and trend item removing post-processing as the interval of histogram distribution, and counting the distribution number of the RR interval sequence based on a preset interval to obtain RR interval histogram data.

Preferably, the regression model is a softmax regression model; establishing a corresponding relation between the RR interval histogram data and a preset fatigue level based on the regression model, wherein the corresponding relation comprises the following steps:

setting various categories of fatigue degree grades, and acquiring a plurality of preset parameters in the RR interval histogram data;

and taking the preset parameters as the input of the softmax regression model, taking each category of the fatigue degree grade as the output of the softmax regression model, and obtaining the corresponding relation between the RR interval histogram data and the preset fatigue degree grade through the training of the softmax regression model.

Preferably, acquiring electrocardiographic signal data to be detected, and performing fatigue detection according to the corresponding relationship, includes:

acquiring a plurality of preset parameters in the electrocardiosignal data to be detected, and forming a plurality of classes according to the preset parameters;

calculating scores of the plurality of classes, calculating a probability for each of the plurality of classes based on the scores of the plurality of classes;

and selecting the class with the maximum probability, and determining the corresponding fatigue level according to the corresponding relation.

The present invention also provides a fatigue detection device, including:

the electrocardiosignal acquisition unit is used for acquiring initial electrocardiosignal data and carrying out pre-filtering, differential amplification and analog-to-digital conversion on the initial electrocardiosignal data to obtain digital electrocardiosignal data;

the electrocardiosignal processing unit is used for preprocessing the digital electrocardiosignal data and extracting the characteristics to obtain RR interval sequence data;

the RR interval data processing unit is used for performing interval filtering, resampling and trend item removing on the RR interval series data to obtain RR interval histogram data;

the relation establishing unit is used for establishing a regression model and establishing a corresponding relation between the RR interval histogram data and a preset fatigue level based on the regression model;

and the fatigue detection unit is used for acquiring the electrocardiosignal data to be detected and carrying out fatigue detection according to the corresponding relation.

The invention also provides an electronic device comprising a memory and a processor, wherein:

the memory is used for storing programs;

the processor, coupled to the memory, is configured to execute the program stored in the memory to implement the steps in the fatigue detection method in any of the above implementations.

The present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which when executed by a processor, can implement the steps in the fatigue detection method in any one of the above-mentioned implementation manners.

The beneficial effects of adopting the above embodiment are: according to the fatigue detection method provided by the invention, the electrocardiosignals of the human body are obtained and classified, the corresponding relation between the electrocardiosignals and the fatigue degree is determined, and the fatigue state can be accurately detected according to the corresponding relation.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow chart diagram illustrating a fatigue detection method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of an embodiment of a fatigue detection apparatus according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an embodiment of an electronic device according to an embodiment of the present invention.

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the following, the terms "first", "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specified.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The invention provides a fatigue detection method, a fatigue detection device, an electronic device and a storage medium, which are respectively described below.

As shown in fig. 1, a schematic flow chart of an embodiment of a fatigue detection method provided in an embodiment of the present invention is shown, where the method includes:

step S101, acquiring initial electrocardiosignal data, and performing pre-filtering, differential amplification and analog-to-digital conversion on the initial electrocardiosignal data to obtain digital electrocardiosignal data;

step S102, preprocessing the digital electrocardiosignal data and extracting characteristics to obtain RR interphase sequence data;

step S103, performing interval filtering, resampling and trend item removing on the RR interval series data to obtain RR interval histogram data;

step S104, creating a regression model, and establishing a corresponding relation between the RR interval histogram data and a preset fatigue level based on the regression model;

and S105, acquiring electrocardiosignal data to be detected, and carrying out fatigue detection according to the corresponding relation.

Specifically, the acquired initial electrocardiosignal data of the human body can be acquired by the electrode and lead wire unit, generally speaking, the output end of the electrode and lead wire unit is connected to the input end of the signal acquisition unit, and the electrode and lead wire unit is connected to the human body and outputs the physiological signal to the signal acquisition unit. The electrode and lead wire units are generally conductors such as limb clamps, chest lead suction balls, electrode plates and the like, and are in close contact with the human body to acquire physiological signals of the human body.

As a preferred embodiment, in step S101, the method specifically includes:

Specifically, the pre-low pass filter is used for filtering some useless high-frequency components and some other spurious signals with the frequency higher than 1/2 times of the sampling frequency, so that the signals meet the nyquist sampling theorem, aliasing distortion cannot occur after sampling, and the pre-low pass filter is output to the differential amplification unit. The differential amplification unit is used for carrying out differential amplification on the input signals and then outputting the signals to the analog-to-digital conversion unit to complete conversion from analog quantity to digital quantity. The differential amplification unit is used for filtering common-mode signals and ensuring the subsequent analog-to-digital conversion precision through amplification.

As a preferred embodiment, in step S102, the preprocessing the digital electrocardiographic signal data specifically includes:

Specifically, the signal preprocessing has the functions of inhibiting 50/60Hz power frequency interference, baseline drift and myoelectric interference outside the electrocardiosignal frequency band, and extracting the electrocardiosignal with small interference and good performance.

As a preferred embodiment, in step S102, the obtaining RR interval sequence data after feature extraction includes:

Specifically, the normal electrocardiogram consists of a P wave, a QRS wave group, a T wave and the like. Each specific wave corresponds to a particular cardiac activity and electrophysiological phase. The R-wave has a higher amplitude than the other waveforms. From the spectrum analysis, the center frequency of the QRS wave group is around 17Hz (this frequency is also called the characteristic frequency of the QRS wave group), and the bandwidth is about 10 Hz. The frequency bands of T wave, P wave, baseline drift, etc. are all outside the low end of the frequency band, and the above is the obvious feature that the QRS complex is distinguished from other waveforms.

Based on the background knowledge, the digital band-pass filter is designed to be a digital band-pass filter, and the digital band-pass filter mainly plays a role in highlighting the amplitude of the QRS wave and restraining the amplitude of the P wave and the T wave and the interference amplitude.

After QRS wave characteristics are extracted, the waveform extracted by the characteristics is compared with a preset threshold value, and whether the waveform is a QRS wave is judged. If the time exceeds the preset threshold value, the time is a QRS wave, and the positions of all R waves are located according to the QRS wave, so that an RR interval sequence is obtained.

As a preferred embodiment, step S103 specifically includes:

setting a sliding window to filter the RR interval sequence data;

It should be noted that the three steps of interval filtering, resampling and trend term removing may be performed simultaneously or in a certain order.

Specifically, during the filtering process of the RR interval, the abnormal fluctuation in the RR interval sequence is mainly caused by the following conditions: (1) due to the long RR interphase caused by missing detection of the QRS wave group, an obvious upward pulse is generated; (2) the false detection of the QRS wave group caused by high and large T waves or noise forms a short RR interval and generates a remarkable downward pulse; (3) the short RR interval and the following long RR interval due to ectopic beats form bidirectional and paired pulses.

The RR interval filtering is to remove abnormal fluctuation in the RR interval sequence and reduce its influence on the subsequent analysis. In RR interval filtering, setting the width of a sliding window as w; for each sequence, its median or mean value a is calculated. Comparing the central point with A, and if the difference value exceeds d% of A, removing the central point; the window position is then shifted and the above calculations are repeated to filter all the data points.

Further, in the RR interval removal trend term, the basic heart rates of different people are different; the basal heart rate of the same person at different periods also varies. In order to maintain the consistency of the fatigue analysis module, the RR interval sequence needs to be subjected to trend removing processing. And when the RR interval removes the trend term, selecting a digital high-pass filter.

As a preferred embodiment, in step S104, the regression model is a softmax regression model; establishing a corresponding relation between the RR interval histogram data and a preset fatigue level based on the regression model, wherein the corresponding relation comprises the following steps:

As a specific example, the degree of human fatigue is divided into N levels according to actual needs, and the N levels are substituted by N values of 1 to N, and the correspondence relationship is shown in the following figure (N in the figure takes 4 as an example).

Degree of fatigue	Easy to use	Is normal	Light and slight	Is higher than
					Representing a numerical value	1	2	3	4

To achieve the above classification, a plurality of preset parameters, such as average heart rate HR, maximum frequency P of histogram distribution, are extracted respectively_maxAnd the width d of the histogram distribution interval as input; in addition, sex and age also have important reference values for fatigue classification, and these two parameters are also used as input. For gender, it was binarized to 0 and 1, corresponding to males and females, respectively.

The human fatigue is classified into N grades, which is a single-label multi-grade classification problem, and is realized by using a Softmax regression model.

When an instance x is given (x is defined by HR, P)_maxVector composed of d, sex, and age), the Softmax regression model first calculates the score sk (x) of the kth class, then applies the score to the Softmax function to estimate the probability of each class, and formula (1) of sk (x) is calculated as:

s_k(x)＝θ_k ^T·x (1)

once the score for each class of samples x is calculated, the probability P that a sample belongs to class k can be estimated by the Softmax function_k: by calculating the sk (x) powers of e and then normalizing them as shown in equation (2).

In the above formula, K represents the total number of categories, which is 4 in the present invention; s (X) represents a score vector containing each class of sample X; sigma (s (x))_kThe probability that instance X belongs to class k given each class score is represented.

Further, the Softmax regression classifier takes the type with the highest estimation probability as the prediction result, as shown in the following formula (3):

y＝argmaxσ(s(x))_k＝argmaxs_k(x)＝argmaxθ_k ^T·x (3)

the argmax operation returns the value of the variable at which the function takes the maximum value. In the above formula, it returns σ (s (x))_kAnd obtaining the corresponding relation between the RR interval histogram data and the preset fatigue level by the maximum k value.

To obtain an accurate corresponding relationship, the pair theta is also required_kA determination is made that may be obtained by training a Softmax regression model on sample data (including the input vector x and corresponding fatigue level).

The expectation of model training is that there is a higher probability on the target class (and therefore lower probability on other classes), and the minimization formula (4) can achieve this goal, which represents the loss function of the current model, called cross entropy, which penalizes the model when it derives a lower probability for the target class. The cross entropy is generally used to measure the matching degree between the category to be measured and the target category:

if the target class for the ith instance is k, then

Otherwise, zero, the loss function is related to theta_kThe gradient vector of (a) is formula (5):

by computing the gradient vectors for each class, then using gradient descent (or other optimization algorithm) to find the parameter matrix Θ that minimizes the loss function.

As a preferred embodiment, step S105 specifically includes:

Specifically, after the parameter matrix Θ is obtained through training, fatigue detection can be performed on the actual input vector. HR and P_maxD, sex and age are input into a formula (1), and the score sk (x) of each type is calculated; and substituting sk (x) into the formula (2), and performing normalization calculation to obtain the probability that the instance x belongs to each class. Finally, the category of the probability with the maximum value is returned by using the formula (3)I.e. the fatigue level.

The fatigue detection method provided by the invention can realize accurate detection of the fatigue state by acquiring the electrocardiosignals of the human body, classifying the electrocardiosignals, determining the corresponding relation between the electrocardiosignals and the fatigue degree and according to the corresponding relation.

In order to better implement the fatigue detection method in the embodiment of the present invention, on the basis of the fatigue detection method, as shown in fig. 2, correspondingly, the embodiment of the present invention further provides a fatigue detection apparatus 200, including:

an electrocardiographic signal acquisition unit 201, configured to acquire initial electrocardiographic signal data, and perform pre-filtering, differential amplification, and analog-to-digital conversion on the initial electrocardiographic signal data to obtain digital electrocardiographic signal data;

the electrocardiosignal processing unit 202 is used for preprocessing the digital electrocardiosignal data and extracting features to obtain RR interval sequence data;

an RR interval data processing unit 203, configured to perform interval filtering, resampling and trend item removal on the RR interval series data to obtain RR interval histogram data;

a relation establishing unit 204, configured to create a regression model, and establish a corresponding relation between the RR interval histogram data and a preset fatigue level based on the regression model;

the fatigue detection unit 205 acquires electrocardiographic signal data to be detected, and performs fatigue detection according to the correspondence.

Here, it should be noted that: the fatigue detection apparatus 200 provided in the foregoing embodiment may implement the technical solutions described in the foregoing method embodiments, and the specific implementation principle of each module or unit may refer to the corresponding content in the foregoing method embodiments, and is not described herein again.

As shown in fig. 3, based on the fatigue detection method, the invention further provides an electronic device 300 accordingly. The electronic device 300 comprises a processor 301, a memory 302 and a display 303. Fig. 3 shows only some of the components of the electronic device 300, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.

The memory 302 may in some embodiments be an internal storage unit of the electronic device 300, such as a hard disk or a memory of the electronic device 300. The memory 302 may also be an external storage device of the electronic device 300 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 300.

Further, the memory 302 may also include both internal storage units and external storage devices of the electronic device 300. The memory 302 is used for storing application software for installing the electronic device 300 and various types of data,

the processor 301 may be a Central Processing Unit (CPU), microprocessor or other data Processing chip in some embodiments, and is used for running program codes stored in the memory 302 or Processing data, such as the fatigue detection method in the present invention.

The display 303 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 303 is used for displaying information at the electronic device 300 and for displaying a visualized user interface. The

components

301 and 303 of the electronic device 300 communicate with each other via a system bus.

In one embodiment, when the processor 301 executes the fatigue detection program 304 in the memory 302, the following steps may be implemented:

and acquiring electrocardiosignal data to be detected, and carrying out fatigue detection according to the corresponding relation. .

It should be understood that: the processor 302, when executing the fatigue detection program 304 in the memory 301, may also implement other functions in addition to the above functions, which may be specifically referred to the description of the corresponding method embodiments above.

Further, the type of the electronic device 300 is not particularly limited in the embodiment of the present invention, and the electronic device 300 may be a portable electronic device such as a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a wearable device, and a laptop computer (laptop). Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices that carry an iOS, android, microsoft, or other operating system. The portable electronic device may also be other portable electronic devices such as laptop computers (laptop) with touch sensitive surfaces (e.g., touch panels), etc. It should also be understood that in other embodiments of the present invention, the electronic device 300 may not be a portable electronic device, but may be a desktop computer having a touch-sensitive surface (e.g., a touch panel).

Accordingly, the present application also provides a computer-readable storage medium, which is used for storing a computer-readable program or instruction, and when the program or instruction is executed by a processor, the program or instruction can implement the method steps or functions provided by the above method embodiments.

Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.

The fatigue detection method, the fatigue detection device, the electronic device and the storage medium provided by the invention are described in detail, and a specific example is applied in the description to explain the principle and the implementation of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A method of fatigue detection, comprising:

2. The fatigue detection method according to claim 1, wherein the step of performing pre-filtering, differential amplification and analog-to-digital conversion on the initial electrocardiographic signal data to obtain digital electrocardiographic signal data comprises:

3. The fatigue detection method of claim 1, wherein preprocessing the digital electrocardiographic signal data comprises:

4. The fatigue detection method according to claim 3, wherein the obtaining of RR interval sequence data after feature extraction comprises:

5. The fatigue detection method according to claim 1, wherein performing interval filtering, resampling and trend term removal on the RR interval series data to obtain RR interval histogram data comprises:

setting a sliding window to filter the RR interval sequence data;

6. The fatigue detection method of claim 1, wherein the regression model is a softmax regression model; establishing a corresponding relation between the RR interval histogram data and a preset fatigue level based on the regression model, wherein the corresponding relation comprises the following steps:

7. The fatigue detection method according to claim 6, wherein acquiring electrocardiographic signal data to be detected, and performing fatigue detection according to the correspondence comprises:

8. A fatigue detecting device, comprising:

9. An electronic device comprising a memory and a processor, wherein,

the memory is used for storing programs;

the processor, coupled to the memory, is configured to execute the program stored in the memory to implement the steps of the fatigue detection method of any of claims 1 to 7.

10. A computer-readable storage medium storing a computer-readable program or instructions, which when executed by a processor, is capable of implementing the steps of the fatigue detection method according to any one of claims 1 to 7.