# Research on Prediction Algorithm of Employees’ Psychological Stress Based on Multifeature Fusion

Authors: Lijun Chang
Journal: Security and Communication Networks
 (2021)

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## Abstract

A multifeature fusion-based enterprise employee psychological stress prediction algorithm is suggested to address the concerns of low prediction accuracy, long duration, and poor results in current psychological stress prediction approaches. Examine ECG signal generation and properties, as well as the notion and causes of heart rate variability. The ECG signal is gathered according to the psychological stress reaction mechanism, and the digital filter is utilized to filter and preprocess the noise interference of the ECG signal. The linear discriminant analysis algorithm extracts the time domain linear features, frequency domain linear features, and nonlinear features of the ECG signal and then selects the ECG signal characteristics. D-S evidence theory is used to fuse the time domain linear characteristics, frequency domain linear characteristics, and nonlinear characteristics of the ECG signal, construct the psychological stress prediction model, obtain the final result of psychological stress state prediction, and realize the psychological stress prediction of enterprise employees, all based on multifeature fusion technology. The results of the experiments reveal that the suggested algorithm has a greater predictive effect on employee psychological stress, allowing it to enhance forecast accuracy and reduce prediction time.


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## Body

## 1. Introduction

When people perceive or understand that they are confronted with a significant and demanding environmental necessity, they experience psychological stress, which manifests itself in a variety of psychological and bodily responses [1]. A sensation of strain and oppression is referred to as stress. It is a huge hazard to people’s lives since some everyday demands cannot be met correctly, putting their health and social lives at risk [2]. Stress frequently causes psychological distress, and people will try to deal with it in order to protect themselves from harm. Moderate pressure is helpful. It has the potential to increase people’s initiative. When pressure exceeds psychological endurance, however, psychological illnesses such as sadness and anxiety can develop, posing a major threat to physical and mental health. Psychological stress is regarded as one of the most significant influences on human health [3]. Because of the fast-paced nature of modern life, business employees are subjected to a variety of life and work pressures; therefore, there will be a high number of patients suffering from mental and psychological disorders. The precise forecast of psychological stress is critical for the continued development of businesses and the health of personnel. Scholars in relevant domains are now conducting research on psychological stress prediction and have arrived at certain theoretical conclusions. In diabetes management, [4] proposed strategies for forecasting physical activity and psychological stress. Analyze the observed physiological indicators and develop new quantified input features for physical activity and acute psychological stress using wristbands that are suitable for free-living mobile personnel. Machine learning technology is used to estimate the type and intensity of physical activity, quantify physical activity and acute psychological stress characteristics, and integrate them as an external input into adaptive system recognition technology to predict physical activity and psychological stress. This method has certain validity. Reference [5] proposed an acute psychological stress prediction system based on mobile context perception, which can predict the stress state of users according to their current context data. The system includes a context-aware stress prediction algorithm as well as a way for early stress intervention. The context-aware stress detection system employs ECG signals to identify the user’s stress state throughout the learning phase. An adaptable and personalized prediction model is produced using a machine learning algorithm and cloud computing service based on the user context collected from their smartphone. This method can adapt to the changing characteristics of user pressure state and surrounding environment. However, the above methods still have the problems of low prediction accuracy, long time, and poor effect.In order to address the aforementioned issues, a multifeature fusion-based method for forecasting employee psychological stress is developed. Based on the psychological stress response mechanism, we performed analysis of the concept and generation of ECG signals and heart rate variability, collection and processing of ECG signals, extraction of the time domain linear characteristics, frequency domain linear characteristics, and nonlinear characteristics of the ECG signal, and selection of the ECG signal feature. The method also uses multifeature fusion technology and D-S evidence theory, fuses numerous characteristics of ECG signals, builds a psychological stress prediction model, obtains psychological stress state prediction results, and implements psychological stress prediction for enterprise personnel. This method has a good prediction effect, and it can significantly enhance forecast accuracy and reduce prediction time.


## 2. Research Basis of ECG Signal

### 2.1. Generation of ECG Signal

The ECG signal is a faint electrical signal that is created by cardiac electrical activity on the body surface and is a thorough expression of cardiac electrical activity on the body surface [6]. Human ECG includes a wealth of physiological and pathological information as a picture representing heart rhythm and cardiac electrical conduction activity. ECG is a reflection of the psychological and emotional state associated with heart activity to some extent. Because of the strong link between ECG and psychological stress, ECG is commonly employed in research.


### 2.2. Characteristics of ECG Signal

The ECG signal is a function derived from the summation of the value of the ECG voltage measured by the electrode contacting the surface of the human body over time and belongs to the time domain waveform signal. The electrocardiogram is the waveform diagram of the amplitude of the electrocardiogram signal changing with time [7]. The waveform of the normal human body ECG is as Figure 1.Figure 1
ECG waveform of normal human body.QRS complexes, P waves, andT waves make up the majority of an ECG waveform in a healthy person. A U wave may emerge following the T wave in some situations. The ECG waveform chart also shows the QT interval, PR interval, ST band, and other parameters. The QRS complex energy accounts for the majority of ECG energy. The following are the main properties of ECG as summarized by the summary:(1)
Low-frequency signal: the frequency of the ECG signal is mainly distributed between 0.05∼100 Hz(2)
Poor stability: the interference of the ECG signal comes from the external environment of the human body itself, such as respiratory interference, myoelectric interference, power frequency interference, and other external crosstalk in the signal acquisition process(3)
Weak signal: the amplitude of ECG often varies around 5μV–50 mV and is on the order of mV at most(4)
Near-field detection: ECG signals are usually not detected when they are a short distance away from the patient


### 2.3. Concept and Causes of Heart Rate Variability

The fluctuation of heart rate during a continuous heartbeat is referred to as heart rate variability (HRV) [8]. Under the influence of the autonomic nervous system, it reflects the balance and tension between the parasensory nerve and the sympathetic nerve (SN), as well as the cardiovascular system (ANS). HRV is linked to psychological stress as well. In many ways, HRV reflects the heart’s adaptability to the external environment. It can offer quantitative data on the autonomic nervous system’s activity and balance, blood pressure, and cardiovascular system performance, as well as reliably and effectively assessing psychological stress. The analysis of HRV includes a series of measurements of the continuous R-R interval changes of the sinus source, providing information about the autonomous tone. Different physiological factors may affect HRV, such as gender, age, circadian rhythm, breathing, and body position.(1)
Age factor: Generally speaking, there is a negative correlation between human short-range HRV and age. With the growth of human age, some HRV characteristic parameters such as high frequency component, standard deviation (SDNN), and low frequency component will decrease.(2)
Gender factor: Among people younger than 30 years old, the HRV value of men is generally higher than that of women. Among people between 30 and 39 years of age, the HRV value of men is lower than that of women.(3)
Circadian rhythm: At night, the parasympathetic nerve activity is significantly reduced. During the day, the vagus nerve is lower than at night, and the sympathetic nerve is higher than at night. Therefore, the diurnal factors should be paid attention to when analyzing HRV.(4)
Pathological factors: The changes between HRV and ANS are related to each other. When the R-R interval fluctuates slowly and the autonomic nervous system is weak, the value of HRV will decrease accordingly. However, when affected by pathological factors, there may be inconsistencies between changes in HRV and changes in the autonomic nervous system.(5)
Psychological factors: The heart rate variability characteristic parameter SDNN of patients with anxiety disorders is generally lower than the normal value of healthy people.


### 2.4. Reaction Mechanism of Psychological Stress

The neurological system of the human body is extremely complicated, and it is divided into two parts: the central nervous system and the peripheral nervous system. The autonomic nervous system and the somatic nervous system are both part of the peripheral nervous system. ANS has a specific association with negative emotions including stress, anxiety, and depression, among others. The sympathetic nervous system and the parasympathetic nervous system, respectively, are responsible for the stress and relaxation responses in the autonomic nervous system [9]. Figure 2 depicts the nervous system’s hierarchical structure.Figure 2
Hierarchical structure of the nervous system.The sympathetic nervous system and the parasympathetic nervous system work together to regulate the appropriate function of human organs and tissues. When there is psychological stress, this equilibrium is disrupted, and the body’s regular mechanisms are disrupted, resulting in aberrant reactions and disorders. When psychological stress is not addressed, it can lead to the development of stress-related disorders, which can be irreversible.


## 2.1. Generation of ECG Signal

The ECG signal is a faint electrical signal that is created by cardiac electrical activity on the body surface and is a thorough expression of cardiac electrical activity on the body surface [6]. Human ECG includes a wealth of physiological and pathological information as a picture representing heart rhythm and cardiac electrical conduction activity. ECG is a reflection of the psychological and emotional state associated with heart activity to some extent. Because of the strong link between ECG and psychological stress, ECG is commonly employed in research.


## 2.2. Characteristics of ECG Signal

The ECG signal is a function derived from the summation of the value of the ECG voltage measured by the electrode contacting the surface of the human body over time and belongs to the time domain waveform signal. The electrocardiogram is the waveform diagram of the amplitude of the electrocardiogram signal changing with time [7]. The waveform of the normal human body ECG is as Figure 1.Figure 1
ECG waveform of normal human body.QRS complexes, P waves, andT waves make up the majority of an ECG waveform in a healthy person. A U wave may emerge following the T wave in some situations. The ECG waveform chart also shows the QT interval, PR interval, ST band, and other parameters. The QRS complex energy accounts for the majority of ECG energy. The following are the main properties of ECG as summarized by the summary:(1)
Low-frequency signal: the frequency of the ECG signal is mainly distributed between 0.05∼100 Hz(2)
Poor stability: the interference of the ECG signal comes from the external environment of the human body itself, such as respiratory interference, myoelectric interference, power frequency interference, and other external crosstalk in the signal acquisition process(3)
Weak signal: the amplitude of ECG often varies around 5μV–50 mV and is on the order of mV at most(4)
Near-field detection: ECG signals are usually not detected when they are a short distance away from the patient


## 2.3. Concept and Causes of Heart Rate Variability

The fluctuation of heart rate during a continuous heartbeat is referred to as heart rate variability (HRV) [8]. Under the influence of the autonomic nervous system, it reflects the balance and tension between the parasensory nerve and the sympathetic nerve (SN), as well as the cardiovascular system (ANS). HRV is linked to psychological stress as well. In many ways, HRV reflects the heart’s adaptability to the external environment. It can offer quantitative data on the autonomic nervous system’s activity and balance, blood pressure, and cardiovascular system performance, as well as reliably and effectively assessing psychological stress. The analysis of HRV includes a series of measurements of the continuous R-R interval changes of the sinus source, providing information about the autonomous tone. Different physiological factors may affect HRV, such as gender, age, circadian rhythm, breathing, and body position.(1)
Age factor: Generally speaking, there is a negative correlation between human short-range HRV and age. With the growth of human age, some HRV characteristic parameters such as high frequency component, standard deviation (SDNN), and low frequency component will decrease.(2)
Gender factor: Among people younger than 30 years old, the HRV value of men is generally higher than that of women. Among people between 30 and 39 years of age, the HRV value of men is lower than that of women.(3)
Circadian rhythm: At night, the parasympathetic nerve activity is significantly reduced. During the day, the vagus nerve is lower than at night, and the sympathetic nerve is higher than at night. Therefore, the diurnal factors should be paid attention to when analyzing HRV.(4)
Pathological factors: The changes between HRV and ANS are related to each other. When the R-R interval fluctuates slowly and the autonomic nervous system is weak, the value of HRV will decrease accordingly. However, when affected by pathological factors, there may be inconsistencies between changes in HRV and changes in the autonomic nervous system.(5)
Psychological factors: The heart rate variability characteristic parameter SDNN of patients with anxiety disorders is generally lower than the normal value of healthy people.


## 2.4. Reaction Mechanism of Psychological Stress

The neurological system of the human body is extremely complicated, and it is divided into two parts: the central nervous system and the peripheral nervous system. The autonomic nervous system and the somatic nervous system are both part of the peripheral nervous system. ANS has a specific association with negative emotions including stress, anxiety, and depression, among others. The sympathetic nervous system and the parasympathetic nervous system, respectively, are responsible for the stress and relaxation responses in the autonomic nervous system [9]. Figure 2 depicts the nervous system’s hierarchical structure.Figure 2
Hierarchical structure of the nervous system.The sympathetic nervous system and the parasympathetic nervous system work together to regulate the appropriate function of human organs and tissues. When there is psychological stress, this equilibrium is disrupted, and the body’s regular mechanisms are disrupted, resulting in aberrant reactions and disorders. When psychological stress is not addressed, it can lead to the development of stress-related disorders, which can be irreversible.


## 3. Processing and Analysis of ECG Signal

### 3.1. ECG Signal Acquisition and Preprocessing

The waveform can be modified to a suitable scale during the acquisition of ECG signal data by altering the horizontal and vertical scales to permit real-time viewing of signal waveform changes. The huge interference data is deleted and the preliminary screening is performed based on the observed actual effect of the gathered signals. Power frequency interference, electromyography interference, and baseline drift are the most common types of noise interference for ECG signals. The noise interference on the ECG signal is removed using digital filters in this research [10]. Figure 3 depicts the ECG preprocessing design method.(1)
Band-pass filter to eliminate noise and other clutter: FIR filter is selected to deal with the interference of EMG noise. The 50 Hz notch filter is designed by window function method, and the window function is Kaiser window. MATLAB has its own Kaiser window function, and its calling format is(1)w=KaiserN,β,whereN represents the length of the window, w represents the returned window function coefficient, and β represents the relationship between the main lobe bandwidth of the window function and the side lobe attenuation.(2)
“Double slope” processing: The idea of “double slope” processing is that the width of QRS complex is relatively fixed, in the range of 0.06∼0.1 s, there are two large slopes in QRS complex, and its width is about 0.03∼0.05 s. As a result, we can just concentrate on the straight-line slope that connects the two samples separated by the width and identify the lowest and highest average slopes in various intervals of a point. Then subtract the left from the right’s maximum slope, or remove the right from the left, and finally get the largest of the two. The equation for calculating the maximum and minimum slopes on each side is as follows:(2)SL,max=maxa≤k≤bz−b−z−b−kk,wherea is equal to the nearest integer of 0.027 fs, b is equal to the nearest integer of 0.063 fs, and fs is the sampling frequency of the ECG signal. Subtract the maximum slope on one side from the minimum slope on the other side to get the “slope difference.” The higher value between these two “slope differences” is used as a variable to measure the steepness Sdiff,max as(3)Sdiff,max=maxSR,max−SL,min,SL,max−SR,min.(3)
Low-pass filtering to eliminate power frequency interference: when collecting ECG, the ECG signal will be interfered by the electromagnetic field, which has a very serious impact on the ECG signal, and it sometimes masks some weak ECG signals. Therefore, a low-pass digital filter can be designed for filtering [11]. Use average sliding filter, it is also a kind of low-pass filter commonly used in ECG signal processing, and its difference equation is(4)yn=1M∑i=0M−1bixn−1.The transfer function expression is(5)Hz=1M∑i=0M−1z−i.The frequency characteristic expression is(6)Hejθ=1M∑n=0M−1e−jθn.After passing the “double slope” processed signal through a low-pass filter, the ECG waveform becomes very smooth.(4)
Sliding window integration: In median filtering, an odd-numbered window of length N is chosen, the gathered data of length N is chosen, the data is sorted using a sorting method, and the intermediate data is found as the point data. Apply the median filter to the selected data using the sliding window, and then calculate the drift component. After deleting the baseline drift component, the output after filtering is the remaining part of the original signal.Figure 3
ECG preprocessing design flow chart.


### 3.2. ECG Signal Feature Extraction

#### 3.2.1. Time Domain Linear Feature Extraction of HRV Signal

Time domain analysis is the simplest method among HRV parameters. It analyzes the R-R interval from a statistical point of view [12]. Geometric figures and statistical analysis are the two most commonly used analysis methods for HRV signals. In the time domain analysis, the following time domain features are mainly studied, and the calculation formula for feature extraction is the following.(1)
Mean:(7)Mean=1N∑n−1Nxn.(2)
Standard deviation (SDNN):(8)SDNN=1N−1∑n−1Nxn−Mean2.(3)
Root mean square of R-R interval difference (RMSSD): RMSSD is used to analyze the degree of dispersion of adjacent R-R intervals. The value of RMSSD is positively correlated with the degree of variation of HRV and the R-R interval. It is often used to reflect the rapidly changing parts of HRV.(9)RMSSD=1N−1∑i=1N−1xi+1−xi2.(4)
SDANN: It means measuring HRV for more than five minutes, dividing the measured signal equally every five minutes, and then calculating the average value of the standard deviation of the signal. The purpose is to remove the fast-changing part of HRV and keep the slow-changing part.(10)SDANN=∑i=1NR¯R¯i−MeanR¯R¯2N.(5)
The percentage of R-R interval greater than 50 ms (pNN50):(11)pNN50=countxi+1−xi>50msN−1,i=1,2,…,N−1.(6)
Second-order difference standard deviation (Twod_SDNN):(12)Twod_SDNN=1N−3∑n=1N−2xn+2−2xn−1+xn−2d_Mean2.(7)
The number of R-R interval differences greater than 50 ms (NN50):(13)NN50=countxi+1−xi>50ms,i=1,2,…,N−1.(8)
Heart rate variation coefficient (CV): used to reflect the magnitude of HRV.(14)CV=SDNNMEAN×100%.(9)
Triangular index: It refers to the ratio of the total numberN of R-R intervals to the number Dt of R-R intervals with the highest density.(15)HRVn=NmaxDt.


#### 3.2.2. Frequency Domain Linear Feature Extraction of HRV Signal

The frequency domain linear feature extraction method of HRV signal decomposition refers to converting frequency research to the time domain by decomposing it into a variety of frequency components with varying energies. HRV data in the frequency domain can be utilized to examine the vagus nerve and sympathetic nerve activity in the human body [13]. The following are the formulas for calculating the characteristic parameters used in frequency domain analysis.(1)
Very low frequency power (a VLF):(16)aVLF=∫f1f2PSDfdf,wherePSDf is the power spectrum of HRV, and f1 and f2 represent the very low frequency of HRV.(2)
Low-frequency power (a LF):(17)aLF=∫f3f4PSDfdf,wheref3 and f4 represent the starting frequency of the HRV low frequency band.(3)
High frequency power (a HF):(18)aHF=∫f5f6PSDfdf,wheref5 and f6 represent the start frequency of the HRV high frequency band.(4)
Total power (aTotal):(19)aTotal=aVLF+aLF+aHF.(5)
The ratio of low frequency power to high frequency power (LFHF):(20)LFHF=aLFaHF.


#### 3.2.3. Nonlinear Feature Extraction of HRV Signal

For the nonlinear analysis of HRV, include Poincare scatter plot, sample entropy (SE), and approximate entropy (ApEn), which are currently frequently used.(1) Poincare Scatter Plot. It uses the abscissa and ordinate to depict points on the coordinate axis, respectively, for the two consecutive R-R intervals in the R-R interval series. The HRV signal’s linear and nonlinear information is represented in the scatter plot, which can be used to explain the dynamic properties of the heart state from a global perspective. The HRV scatter diagram of heart failure patients has a fan form, and the distribution area can be utilized to show total HRV variations.(2) Approximate Entropy (ApEn). It means that when multiple similar vectors increase from the k dimensional space to the k+1 dimensional space, there is still a higher probability of similarity. The algorithm steps of approximate entropy are the following.Step 1.
Set the original signal asxi,i=1,2,…,N, and reconstruct the m dimensional vector according to the following formula. Expressed by yi, yi,i=1,2,…,M,M=N−m+1, namely,(21)yi=xi,xi+1,xi+2,…,xi+m−1,where m represents the embedding dimension.Step 2.
Calculate the maximum contribution component distanceDyi,yj, that is, the maximum Euclidean distance dyi,yj between yi and yj:(22)Dyi,yj=maxyi+k−xj+k.Step 3.
Calculate the probability measureCimr, that is, the ratio of the number of Dyi,yj<rNmi to N−m+1:(23)Crmr=NmiN−m+1.Step 4.
Calculate the log mean ofCimr:(24)ϕmr=∑i=1N−m+1lnCimrN−m+1.Step 5.
Change the embedding dimension tom+1. Repeat Steps 1 to 4 to get Cim+1r and ϕm+1r. The myopia entropy is(25)ApEnm,r=ϕmr−ϕm+1r.
The size of the approximate entropy is related toN,m,r. When m=2,r=0.1∼0.25 times the standard deviation, it has better estimation characteristics. Approximate entropy measures the regularity and complexity of time series.
(3) Sample Entropy (SE). SE is a measure of the time complexity of a signal, and there is a positive correlation between them. The sample entropy is interpreted and used the same as the approximate entropy, and the method of obtaining it is similar to the approximate entropy, which can be expressed as(26)SaEnm,r=−lnϕm+1rϕmr.


### 3.3. ECG Signal Feature Selection

High-dimensional feature sets may contain redundant information, which not only increases computation time but also lowers data classification accuracy. Feature selection, also known as dimension reduction, is usually done after feature extraction. To achieve high category separability, the linear discriminant analysis (LDA) algorithm is used in this research to project the feature vector to the optimum space [14]. A frequent feature selection approach is the LDA algorithm, also known as Fisher discriminant analysis. The basic idea is to project the multidimensional model into the best vector space so that the dispersion between classes is as large as possible, and the dispersion within the classes is as small as possible to achieve the best class separability. The specific steps of the algorithm are described as follows.Consider the variablexii=1,2,…,m; the mean value of the variable of the i category can be obtained according to the following formula:(27)ui=1ni∑x∈classix,where ni is the number of variables belonging to the i category. The degree of dispersion matrix between categories is expressed as(28)sb=∑i=1cniui−uui−uT.The degree of dispersion matrix within the category is expressed as(29)sw=∑i=1c∑xk∈classiui−xkui−xkT,where c is the total number of categories. The formula of Fisher identification criteria is(30)Jφ=φTsbφφTswφ,where φ is a vector. The criterion of Fisher discriminant analysis is to make Jφ reach the maximum; that is, the dispersion within the class is the smallest, and the dispersion between the classes is the largest.


## 3.1. ECG Signal Acquisition and Preprocessing

The waveform can be modified to a suitable scale during the acquisition of ECG signal data by altering the horizontal and vertical scales to permit real-time viewing of signal waveform changes. The huge interference data is deleted and the preliminary screening is performed based on the observed actual effect of the gathered signals. Power frequency interference, electromyography interference, and baseline drift are the most common types of noise interference for ECG signals. The noise interference on the ECG signal is removed using digital filters in this research [10]. Figure 3 depicts the ECG preprocessing design method.(1)
Band-pass filter to eliminate noise and other clutter: FIR filter is selected to deal with the interference of EMG noise. The 50 Hz notch filter is designed by window function method, and the window function is Kaiser window. MATLAB has its own Kaiser window function, and its calling format is(1)w=KaiserN,β,whereN represents the length of the window, w represents the returned window function coefficient, and β represents the relationship between the main lobe bandwidth of the window function and the side lobe attenuation.(2)
“Double slope” processing: The idea of “double slope” processing is that the width of QRS complex is relatively fixed, in the range of 0.06∼0.1 s, there are two large slopes in QRS complex, and its width is about 0.03∼0.05 s. As a result, we can just concentrate on the straight-line slope that connects the two samples separated by the width and identify the lowest and highest average slopes in various intervals of a point. Then subtract the left from the right’s maximum slope, or remove the right from the left, and finally get the largest of the two. The equation for calculating the maximum and minimum slopes on each side is as follows:(2)SL,max=maxa≤k≤bz−b−z−b−kk,wherea is equal to the nearest integer of 0.027 fs, b is equal to the nearest integer of 0.063 fs, and fs is the sampling frequency of the ECG signal. Subtract the maximum slope on one side from the minimum slope on the other side to get the “slope difference.” The higher value between these two “slope differences” is used as a variable to measure the steepness Sdiff,max as(3)Sdiff,max=maxSR,max−SL,min,SL,max−SR,min.(3)
Low-pass filtering to eliminate power frequency interference: when collecting ECG, the ECG signal will be interfered by the electromagnetic field, which has a very serious impact on the ECG signal, and it sometimes masks some weak ECG signals. Therefore, a low-pass digital filter can be designed for filtering [11]. Use average sliding filter, it is also a kind of low-pass filter commonly used in ECG signal processing, and its difference equation is(4)yn=1M∑i=0M−1bixn−1.The transfer function expression is(5)Hz=1M∑i=0M−1z−i.The frequency characteristic expression is(6)Hejθ=1M∑n=0M−1e−jθn.After passing the “double slope” processed signal through a low-pass filter, the ECG waveform becomes very smooth.(4)
Sliding window integration: In median filtering, an odd-numbered window of length N is chosen, the gathered data of length N is chosen, the data is sorted using a sorting method, and the intermediate data is found as the point data. Apply the median filter to the selected data using the sliding window, and then calculate the drift component. After deleting the baseline drift component, the output after filtering is the remaining part of the original signal.Figure 3
ECG preprocessing design flow chart.


## 3.2. ECG Signal Feature Extraction

### 3.2.1. Time Domain Linear Feature Extraction of HRV Signal

Time domain analysis is the simplest method among HRV parameters. It analyzes the R-R interval from a statistical point of view [12]. Geometric figures and statistical analysis are the two most commonly used analysis methods for HRV signals. In the time domain analysis, the following time domain features are mainly studied, and the calculation formula for feature extraction is the following.(1)
Mean:(7)Mean=1N∑n−1Nxn.(2)
Standard deviation (SDNN):(8)SDNN=1N−1∑n−1Nxn−Mean2.(3)
Root mean square of R-R interval difference (RMSSD): RMSSD is used to analyze the degree of dispersion of adjacent R-R intervals. The value of RMSSD is positively correlated with the degree of variation of HRV and the R-R interval. It is often used to reflect the rapidly changing parts of HRV.(9)RMSSD=1N−1∑i=1N−1xi+1−xi2.(4)
SDANN: It means measuring HRV for more than five minutes, dividing the measured signal equally every five minutes, and then calculating the average value of the standard deviation of the signal. The purpose is to remove the fast-changing part of HRV and keep the slow-changing part.(10)SDANN=∑i=1NR¯R¯i−MeanR¯R¯2N.(5)
The percentage of R-R interval greater than 50 ms (pNN50):(11)pNN50=countxi+1−xi>50msN−1,i=1,2,…,N−1.(6)
Second-order difference standard deviation (Twod_SDNN):(12)Twod_SDNN=1N−3∑n=1N−2xn+2−2xn−1+xn−2d_Mean2.(7)
The number of R-R interval differences greater than 50 ms (NN50):(13)NN50=countxi+1−xi>50ms,i=1,2,…,N−1.(8)
Heart rate variation coefficient (CV): used to reflect the magnitude of HRV.(14)CV=SDNNMEAN×100%.(9)
Triangular index: It refers to the ratio of the total numberN of R-R intervals to the number Dt of R-R intervals with the highest density.(15)HRVn=NmaxDt.


### 3.2.2. Frequency Domain Linear Feature Extraction of HRV Signal

The frequency domain linear feature extraction method of HRV signal decomposition refers to converting frequency research to the time domain by decomposing it into a variety of frequency components with varying energies. HRV data in the frequency domain can be utilized to examine the vagus nerve and sympathetic nerve activity in the human body [13]. The following are the formulas for calculating the characteristic parameters used in frequency domain analysis.(1)
Very low frequency power (a VLF):(16)aVLF=∫f1f2PSDfdf,wherePSDf is the power spectrum of HRV, and f1 and f2 represent the very low frequency of HRV.(2)
Low-frequency power (a LF):(17)aLF=∫f3f4PSDfdf,wheref3 and f4 represent the starting frequency of the HRV low frequency band.(3)
High frequency power (a HF):(18)aHF=∫f5f6PSDfdf,wheref5 and f6 represent the start frequency of the HRV high frequency band.(4)
Total power (aTotal):(19)aTotal=aVLF+aLF+aHF.(5)
The ratio of low frequency power to high frequency power (LFHF):(20)LFHF=aLFaHF.


### 3.2.3. Nonlinear Feature Extraction of HRV Signal

For the nonlinear analysis of HRV, include Poincare scatter plot, sample entropy (SE), and approximate entropy (ApEn), which are currently frequently used.(1) Poincare Scatter Plot. It uses the abscissa and ordinate to depict points on the coordinate axis, respectively, for the two consecutive R-R intervals in the R-R interval series. The HRV signal’s linear and nonlinear information is represented in the scatter plot, which can be used to explain the dynamic properties of the heart state from a global perspective. The HRV scatter diagram of heart failure patients has a fan form, and the distribution area can be utilized to show total HRV variations.(2) Approximate Entropy (ApEn). It means that when multiple similar vectors increase from the k dimensional space to the k+1 dimensional space, there is still a higher probability of similarity. The algorithm steps of approximate entropy are the following.Step 1.
Set the original signal asxi,i=1,2,…,N, and reconstruct the m dimensional vector according to the following formula. Expressed by yi, yi,i=1,2,…,M,M=N−m+1, namely,(21)yi=xi,xi+1,xi+2,…,xi+m−1,where m represents the embedding dimension.Step 2.
Calculate the maximum contribution component distanceDyi,yj, that is, the maximum Euclidean distance dyi,yj between yi and yj:(22)Dyi,yj=maxyi+k−xj+k.Step 3.
Calculate the probability measureCimr, that is, the ratio of the number of Dyi,yj<rNmi to N−m+1:(23)Crmr=NmiN−m+1.Step 4.
Calculate the log mean ofCimr:(24)ϕmr=∑i=1N−m+1lnCimrN−m+1.Step 5.
Change the embedding dimension tom+1. Repeat Steps 1 to 4 to get Cim+1r and ϕm+1r. The myopia entropy is(25)ApEnm,r=ϕmr−ϕm+1r.
The size of the approximate entropy is related toN,m,r. When m=2,r=0.1∼0.25 times the standard deviation, it has better estimation characteristics. Approximate entropy measures the regularity and complexity of time series.
(3) Sample Entropy (SE). SE is a measure of the time complexity of a signal, and there is a positive correlation between them. The sample entropy is interpreted and used the same as the approximate entropy, and the method of obtaining it is similar to the approximate entropy, which can be expressed as(26)SaEnm,r=−lnϕm+1rϕmr.


## 3.2.1. Time Domain Linear Feature Extraction of HRV Signal

Time domain analysis is the simplest method among HRV parameters. It analyzes the R-R interval from a statistical point of view [12]. Geometric figures and statistical analysis are the two most commonly used analysis methods for HRV signals. In the time domain analysis, the following time domain features are mainly studied, and the calculation formula for feature extraction is the following.(1)
Mean:(7)Mean=1N∑n−1Nxn.(2)
Standard deviation (SDNN):(8)SDNN=1N−1∑n−1Nxn−Mean2.(3)
Root mean square of R-R interval difference (RMSSD): RMSSD is used to analyze the degree of dispersion of adjacent R-R intervals. The value of RMSSD is positively correlated with the degree of variation of HRV and the R-R interval. It is often used to reflect the rapidly changing parts of HRV.(9)RMSSD=1N−1∑i=1N−1xi+1−xi2.(4)
SDANN: It means measuring HRV for more than five minutes, dividing the measured signal equally every five minutes, and then calculating the average value of the standard deviation of the signal. The purpose is to remove the fast-changing part of HRV and keep the slow-changing part.(10)SDANN=∑i=1NR¯R¯i−MeanR¯R¯2N.(5)
The percentage of R-R interval greater than 50 ms (pNN50):(11)pNN50=countxi+1−xi>50msN−1,i=1,2,…,N−1.(6)
Second-order difference standard deviation (Twod_SDNN):(12)Twod_SDNN=1N−3∑n=1N−2xn+2−2xn−1+xn−2d_Mean2.(7)
The number of R-R interval differences greater than 50 ms (NN50):(13)NN50=countxi+1−xi>50ms,i=1,2,…,N−1.(8)
Heart rate variation coefficient (CV): used to reflect the magnitude of HRV.(14)CV=SDNNMEAN×100%.(9)
Triangular index: It refers to the ratio of the total numberN of R-R intervals to the number Dt of R-R intervals with the highest density.(15)HRVn=NmaxDt.


## 3.2.2. Frequency Domain Linear Feature Extraction of HRV Signal

The frequency domain linear feature extraction method of HRV signal decomposition refers to converting frequency research to the time domain by decomposing it into a variety of frequency components with varying energies. HRV data in the frequency domain can be utilized to examine the vagus nerve and sympathetic nerve activity in the human body [13]. The following are the formulas for calculating the characteristic parameters used in frequency domain analysis.(1)
Very low frequency power (a VLF):(16)aVLF=∫f1f2PSDfdf,wherePSDf is the power spectrum of HRV, and f1 and f2 represent the very low frequency of HRV.(2)
Low-frequency power (a LF):(17)aLF=∫f3f4PSDfdf,wheref3 and f4 represent the starting frequency of the HRV low frequency band.(3)
High frequency power (a HF):(18)aHF=∫f5f6PSDfdf,wheref5 and f6 represent the start frequency of the HRV high frequency band.(4)
Total power (aTotal):(19)aTotal=aVLF+aLF+aHF.(5)
The ratio of low frequency power to high frequency power (LFHF):(20)LFHF=aLFaHF.


## 3.2.3. Nonlinear Feature Extraction of HRV Signal

For the nonlinear analysis of HRV, include Poincare scatter plot, sample entropy (SE), and approximate entropy (ApEn), which are currently frequently used.(1) Poincare Scatter Plot. It uses the abscissa and ordinate to depict points on the coordinate axis, respectively, for the two consecutive R-R intervals in the R-R interval series. The HRV signal’s linear and nonlinear information is represented in the scatter plot, which can be used to explain the dynamic properties of the heart state from a global perspective. The HRV scatter diagram of heart failure patients has a fan form, and the distribution area can be utilized to show total HRV variations.(2) Approximate Entropy (ApEn). It means that when multiple similar vectors increase from the k dimensional space to the k+1 dimensional space, there is still a higher probability of similarity. The algorithm steps of approximate entropy are the following.Step 1.
Set the original signal asxi,i=1,2,…,N, and reconstruct the m dimensional vector according to the following formula. Expressed by yi, yi,i=1,2,…,M,M=N−m+1, namely,(21)yi=xi,xi+1,xi+2,…,xi+m−1,where m represents the embedding dimension.Step 2.
Calculate the maximum contribution component distanceDyi,yj, that is, the maximum Euclidean distance dyi,yj between yi and yj:(22)Dyi,yj=maxyi+k−xj+k.Step 3.
Calculate the probability measureCimr, that is, the ratio of the number of Dyi,yj<rNmi to N−m+1:(23)Crmr=NmiN−m+1.Step 4.
Calculate the log mean ofCimr:(24)ϕmr=∑i=1N−m+1lnCimrN−m+1.Step 5.
Change the embedding dimension tom+1. Repeat Steps 1 to 4 to get Cim+1r and ϕm+1r. The myopia entropy is(25)ApEnm,r=ϕmr−ϕm+1r.
The size of the approximate entropy is related toN,m,r. When m=2,r=0.1∼0.25 times the standard deviation, it has better estimation characteristics. Approximate entropy measures the regularity and complexity of time series.
(3) Sample Entropy (SE). SE is a measure of the time complexity of a signal, and there is a positive correlation between them. The sample entropy is interpreted and used the same as the approximate entropy, and the method of obtaining it is similar to the approximate entropy, which can be expressed as(26)SaEnm,r=−lnϕm+1rϕmr.


## 3.3. ECG Signal Feature Selection

High-dimensional feature sets may contain redundant information, which not only increases computation time but also lowers data classification accuracy. Feature selection, also known as dimension reduction, is usually done after feature extraction. To achieve high category separability, the linear discriminant analysis (LDA) algorithm is used in this research to project the feature vector to the optimum space [14]. A frequent feature selection approach is the LDA algorithm, also known as Fisher discriminant analysis. The basic idea is to project the multidimensional model into the best vector space so that the dispersion between classes is as large as possible, and the dispersion within the classes is as small as possible to achieve the best class separability. The specific steps of the algorithm are described as follows.Consider the variablexii=1,2,…,m; the mean value of the variable of the i category can be obtained according to the following formula:(27)ui=1ni∑x∈classix,where ni is the number of variables belonging to the i category. The degree of dispersion matrix between categories is expressed as(28)sb=∑i=1cniui−uui−uT.The degree of dispersion matrix within the category is expressed as(29)sw=∑i=1c∑xk∈classiui−xkui−xkT,where c is the total number of categories. The formula of Fisher identification criteria is(30)Jφ=φTsbφφTswφ,where φ is a vector. The criterion of Fisher discriminant analysis is to make Jφ reach the maximum; that is, the dispersion within the class is the smallest, and the dispersion between the classes is the largest.


## 4. Construction of Psychological Stress Prediction Model Based on Multifeature Fusion

A psychological stress prediction model based on multifeature fusion is created, and the data processing results are fused to get the final result of psychological stress state prediction, aiming at the processing operations of ECG signal preprocessing, feature extraction, and selection.


### 4.1. Multifeature Fusion Technology

D-S evidence theory is employed for multifeature fusion in multifeature fusion technology [15], and three separate feature groups are used for feature level data fusion: time domain linear features, frequency domain linear features, and nonlinear features. After filtering, analyzing, and preprocessing the three types of features, three separate features are extracted and selected, and multifeature fusion is performed. The fusion model is then utilized to combine the relevant evidence bodies and predict corporate employee psychological strain. Figure 4 depicts the flow of a multifeature fusion-based enterprise employee psychological stress prediction system.Figure 4
Flow chart of the prediction algorithm of employee psychological stress based on multifeature fusion.


### 4.2. Construction of Psychological Stress Prediction Model

The fusion of several features is comparable to the fusion of multiple evidence bodies in the process of building a psychological pressure prediction model based on the D-S evidence theory and each feature might correspond to an independent local judgment process. The D-S evidence theory employs uncertainty reasoning and is based on the basic probability distribution function. To generate appropriate fusion results, a multifeature fusion function is created based on the basic probability distribution function mass. Assuming thatC is an identification space and the entire subset is 2C, then m:2C⟶0,1 is called a basic probability assignment; that is, the target that meets the conditions in the identification space is assigned probabilities according to a certain algorithm. The assigned probability requires the following conditions:(31)mφ=0,0<mA<1,∑A⊆CmA=1,where mA represents the basic probability assignment of each predicted target A. The trust function of the predicted target A is expressed as BelA, which is the lower bound function of the trust level of the predicted target A. The relationship between the trust function and the basic probability assignment is as follows:(32)BelA=∑B⊆AmB,∀A⊆2C.The likelihood function of the predicted targetA is expressed as PlsA, which is the upper limit function of the confidence that the predicted target A is not false. Based on the recognition space that the target opposite to A is considered false, the calculation formula of PlsA is as follows:(33)PlsA=1−BelA¯,∀A⊆2C.The uncertainty of the predicted targetA is composed of the likelihood function PlsA and the trust function BelA, that is, the difference between the probability that the trust target A is not false and the probability that the trust A is true, expressed as mΔ:(34)mΔ=PlsA−BelA.When there are multiple evidence bodies, the probability assignment of the predicted target is the main body of the fusion process by combining multiple evidence bodies according to a certain combination rule. Fusion of multiple evidence bodies to obtain the trust function ofA is expressed as(35)mA=11−K∑αi∩βj=Am1αim2βj,where K represents the degree of conflict between the evidence bodies. When the value of K is closer to 1, the conflict between the evidence bodies is greater. When K≥1, it means that the evidence is completely repelled. Therefore, the conflict degree value K between the evidence bodies must be calculated before the fusion, and only when the value of K is within the range of 0,1 can the relevant evidence bodies be fused. The calculation method of K is as follows:(36)K=1−∑αi∩βj=ϕmiαimjβj.For the combination of multiple evidence bodies, it satisfies the commutative law and associative law of the algorithm. Therefore, the combination rules given above can be used to streamline the fusion combination of the evidence body in a way that satisfies the commutative law and the associative law. In the identification space of this article, there are only two prediction targets, namely, psychological pressure and no psychological pressure, so the likelihood functionPlsA and the trust function BelA are the same, and the uncertainty probability is zero. Through the above steps, the psychological stress prediction of enterprise employees is realized.


## 4.1. Multifeature Fusion Technology

D-S evidence theory is employed for multifeature fusion in multifeature fusion technology [15], and three separate feature groups are used for feature level data fusion: time domain linear features, frequency domain linear features, and nonlinear features. After filtering, analyzing, and preprocessing the three types of features, three separate features are extracted and selected, and multifeature fusion is performed. The fusion model is then utilized to combine the relevant evidence bodies and predict corporate employee psychological strain. Figure 4 depicts the flow of a multifeature fusion-based enterprise employee psychological stress prediction system.Figure 4
Flow chart of the prediction algorithm of employee psychological stress based on multifeature fusion.


## 4.2. Construction of Psychological Stress Prediction Model

The fusion of several features is comparable to the fusion of multiple evidence bodies in the process of building a psychological pressure prediction model based on the D-S evidence theory and each feature might correspond to an independent local judgment process. The D-S evidence theory employs uncertainty reasoning and is based on the basic probability distribution function. To generate appropriate fusion results, a multifeature fusion function is created based on the basic probability distribution function mass. Assuming thatC is an identification space and the entire subset is 2C, then m:2C⟶0,1 is called a basic probability assignment; that is, the target that meets the conditions in the identification space is assigned probabilities according to a certain algorithm. The assigned probability requires the following conditions:(31)mφ=0,0<mA<1,∑A⊆CmA=1,where mA represents the basic probability assignment of each predicted target A. The trust function of the predicted target A is expressed as BelA, which is the lower bound function of the trust level of the predicted target A. The relationship between the trust function and the basic probability assignment is as follows:(32)BelA=∑B⊆AmB,∀A⊆2C.The likelihood function of the predicted targetA is expressed as PlsA, which is the upper limit function of the confidence that the predicted target A is not false. Based on the recognition space that the target opposite to A is considered false, the calculation formula of PlsA is as follows:(33)PlsA=1−BelA¯,∀A⊆2C.The uncertainty of the predicted targetA is composed of the likelihood function PlsA and the trust function BelA, that is, the difference between the probability that the trust target A is not false and the probability that the trust A is true, expressed as mΔ:(34)mΔ=PlsA−BelA.When there are multiple evidence bodies, the probability assignment of the predicted target is the main body of the fusion process by combining multiple evidence bodies according to a certain combination rule. Fusion of multiple evidence bodies to obtain the trust function ofA is expressed as(35)mA=11−K∑αi∩βj=Am1αim2βj,where K represents the degree of conflict between the evidence bodies. When the value of K is closer to 1, the conflict between the evidence bodies is greater. When K≥1, it means that the evidence is completely repelled. Therefore, the conflict degree value K between the evidence bodies must be calculated before the fusion, and only when the value of K is within the range of 0,1 can the relevant evidence bodies be fused. The calculation method of K is as follows:(36)K=1−∑αi∩βj=ϕmiαimjβj.For the combination of multiple evidence bodies, it satisfies the commutative law and associative law of the algorithm. Therefore, the combination rules given above can be used to streamline the fusion combination of the evidence body in a way that satisfies the commutative law and the associative law. In the identification space of this article, there are only two prediction targets, namely, psychological pressure and no psychological pressure, so the likelihood functionPlsA and the trust function BelA are the same, and the uncertainty probability is zero. Through the above steps, the psychological stress prediction of enterprise employees is realized.


## 5. Experimental Analysis

### 5.1. Experimental Environment and Data Set

The ECG signals required in the experiment are collected by ECG signal acquisition equipment, which can collect 12-lead ECG signals, including standard leads I and II, III, chest leads V1V6, limb leads a VR, a VF, and a VL, and the collected signals of multiple leads are used to verify the effectiveness of the enterprise employee psychological stress prediction algorithm based on multifeature fusion. Nine employees from a certain company were used as the subjects of this experiment. The subjects were all healthy and without hearing impairment and were right-handed, with no emotional expression obstacles, and no significant emotional fluctuations before being tested, and they filled out an informed consent form.ECG data was gathered as part of this trial. ECG signals from eight channels are delivered as data packets. The ECG data is gathered by 8 leads in sequence, and the data packet contains 6 8-bit header data. A lead off flag, the last 8-bit lead off flag, and an 8-bit checksum are all included in the data of each lead. The ECG data collected in the experiment is processed according to the format of data packet to obtain the standard data set. The calculation process is realized by MATLAB software. The calculation process is data type conversion, lead data splitting and grouping, high ground data combination, standard signal calculation, and display waveform. In the simulation experiment, 120 samples are taken as the training set and 50 samples as the test set.


### 5.2. Comparison of Predictive Effects of Psychological Stress among Employees

In order to verify the prediction effect of the proposed algorithm, the algorithm in [4] (Signal Empirical Mode Decomposition Processing Method) and the algorithm in [5] (Adaptive Algorithm) are compared with the proposed algorithm, respectively, and the comparison results of the prediction effect of enterprise employees’ psychological stress of different algorithms are obtained as Figure 5.Figure 5
Comparison results of different algorithms for predicting the psychological stress of employees.Figure5 shows that the prediction results of enterprise employees’ psychological stress of the algorithm in [4] are large, with a certain deviation from the actual prediction value, whereas the prediction results of enterprise employees’ psychological stress of the algorithm in [5] are small, with a large deviation from the actual prediction value. The prediction results of enterprise employees’ psychological stress by the proposed algorithm are basically consistent with the actual prediction results of enterprise employees’ psychological stress. It can be seen that, compared with the algorithms in [4] and the algorithms in [5], the proposed algorithm has better prediction effect on enterprise employees’ psychological stress.


### 5.3. Comparison of Prediction Time of Psychological Stress among Employees

Further verify the prediction time of the proposed algorithm, compare the algorithm in [4], the algorithm in [5], and the proposed algorithm, respectively, and get the comparison results of psychological stress prediction time of enterprise employees with different algorithms as Figure 6.Figure 6
Comparison results of employee psychological stress prediction time based on different algorithms.As can be seen in Figure6, the time for estimating the psychological stress of employees of different algorithms increases as the sample data of the test set grows. When the test set data is 50, the algorithm in [4] predicts enterprise employees’ psychological stress in 47 seconds, the algorithm in [5] predicts enterprise employees’ psychological stress in 56 seconds, and the proposed algorithm predicts enterprise employees’ psychological stress in only 24 seconds. It can be seen that, compared with the algorithm in [4] and the algorithm in [5], the proposed algorithm has a shorter prediction time of enterprise employees’ psychological stress.


### 5.4. Comparison of Prediction Accuracy of Psychological Stress among Employees

The proposed algorithm’s prediction accuracy is further evaluated on this premise, and the ROC curve is chosen as the assessment index for the psychological stress prediction accuracy of enterprise employees. The AUC value is defined as the area under the ROC curve that is enclosed by the coordinate axis. The closer the AUC number is to 1.0, the more reliable the prediction method and the more accurate the prediction. On the other hand, the lower the authenticity of the prediction approach, the lower the prediction accuracy. By comparing the algorithm in [4], the algorithm in [5] and the proposed algorithm, we get the comparison results of the prediction accuracy of enterprise employees’ psychological stress with different algorithms as Figure 7.Figure 7
Comparison results of different algorithms for predicting the accuracy of employees’ psychological stress.The AUC value under the ROC curve of the algorithm in [4] is 0.642, the AUC value under the ROC curve of the algorithm in [5] is 0.774, and the AUC value under the ROC curve of the proposed algorithm is 0.927, as can be seen in Figure 7. As a result, when compared to the methods in [4, 5], the proposed algorithm’s AUC value under the ROC curve is closer to 1.0, and the proposed algorithm’s prediction accuracy of enterprise employees’ psychological stress is higher.


## 5.1. Experimental Environment and Data Set

The ECG signals required in the experiment are collected by ECG signal acquisition equipment, which can collect 12-lead ECG signals, including standard leads I and II, III, chest leads V1V6, limb leads a VR, a VF, and a VL, and the collected signals of multiple leads are used to verify the effectiveness of the enterprise employee psychological stress prediction algorithm based on multifeature fusion. Nine employees from a certain company were used as the subjects of this experiment. The subjects were all healthy and without hearing impairment and were right-handed, with no emotional expression obstacles, and no significant emotional fluctuations before being tested, and they filled out an informed consent form.ECG data was gathered as part of this trial. ECG signals from eight channels are delivered as data packets. The ECG data is gathered by 8 leads in sequence, and the data packet contains 6 8-bit header data. A lead off flag, the last 8-bit lead off flag, and an 8-bit checksum are all included in the data of each lead. The ECG data collected in the experiment is processed according to the format of data packet to obtain the standard data set. The calculation process is realized by MATLAB software. The calculation process is data type conversion, lead data splitting and grouping, high ground data combination, standard signal calculation, and display waveform. In the simulation experiment, 120 samples are taken as the training set and 50 samples as the test set.


## 5.2. Comparison of Predictive Effects of Psychological Stress among Employees

In order to verify the prediction effect of the proposed algorithm, the algorithm in [4] (Signal Empirical Mode Decomposition Processing Method) and the algorithm in [5] (Adaptive Algorithm) are compared with the proposed algorithm, respectively, and the comparison results of the prediction effect of enterprise employees’ psychological stress of different algorithms are obtained as Figure 5.Figure 5
Comparison results of different algorithms for predicting the psychological stress of employees.Figure5 shows that the prediction results of enterprise employees’ psychological stress of the algorithm in [4] are large, with a certain deviation from the actual prediction value, whereas the prediction results of enterprise employees’ psychological stress of the algorithm in [5] are small, with a large deviation from the actual prediction value. The prediction results of enterprise employees’ psychological stress by the proposed algorithm are basically consistent with the actual prediction results of enterprise employees’ psychological stress. It can be seen that, compared with the algorithms in [4] and the algorithms in [5], the proposed algorithm has better prediction effect on enterprise employees’ psychological stress.


## 5.3. Comparison of Prediction Time of Psychological Stress among Employees

Further verify the prediction time of the proposed algorithm, compare the algorithm in [4], the algorithm in [5], and the proposed algorithm, respectively, and get the comparison results of psychological stress prediction time of enterprise employees with different algorithms as Figure 6.Figure 6
Comparison results of employee psychological stress prediction time based on different algorithms.As can be seen in Figure6, the time for estimating the psychological stress of employees of different algorithms increases as the sample data of the test set grows. When the test set data is 50, the algorithm in [4] predicts enterprise employees’ psychological stress in 47 seconds, the algorithm in [5] predicts enterprise employees’ psychological stress in 56 seconds, and the proposed algorithm predicts enterprise employees’ psychological stress in only 24 seconds. It can be seen that, compared with the algorithm in [4] and the algorithm in [5], the proposed algorithm has a shorter prediction time of enterprise employees’ psychological stress.


## 5.4. Comparison of Prediction Accuracy of Psychological Stress among Employees

The proposed algorithm’s prediction accuracy is further evaluated on this premise, and the ROC curve is chosen as the assessment index for the psychological stress prediction accuracy of enterprise employees. The AUC value is defined as the area under the ROC curve that is enclosed by the coordinate axis. The closer the AUC number is to 1.0, the more reliable the prediction method and the more accurate the prediction. On the other hand, the lower the authenticity of the prediction approach, the lower the prediction accuracy. By comparing the algorithm in [4], the algorithm in [5] and the proposed algorithm, we get the comparison results of the prediction accuracy of enterprise employees’ psychological stress with different algorithms as Figure 7.Figure 7
Comparison results of different algorithms for predicting the accuracy of employees’ psychological stress.The AUC value under the ROC curve of the algorithm in [4] is 0.642, the AUC value under the ROC curve of the algorithm in [5] is 0.774, and the AUC value under the ROC curve of the proposed algorithm is 0.927, as can be seen in Figure 7. As a result, when compared to the methods in [4, 5], the proposed algorithm’s AUC value under the ROC curve is closer to 1.0, and the proposed algorithm’s prediction accuracy of enterprise employees’ psychological stress is higher.


## 6. Conclusion

The multifeature fusion-based enterprise employee psychological stress prediction algorithm suggested in this study fully utilizes the benefits of multifeature fusion technology. It has a high psychological stress prediction accuracy for enterprise employees, can effectively reduce enterprise employee psychological stress prediction time, and has a good psychological stress prediction effect. However, this method ignores gender and age when predicting employee psychological stress, instead focusing on the impact of key characteristics such as ECG asymmetry and psychological stress level on prediction outcomes. As a result, in the future study, we will take into account the effects of gender and age, increase the sample size, investigate the mapping link between psychological stress level and other parameters, and construct an online psychological stress detection system.



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*Source: 6917191-2021-10-14.xml*