Abstract
Today, excessive psychological stress has become a universal threat to humans. That stress can heavily affect work and study when a person repeatedly is exposed to high stress. If that exposure is long enough, it can even cause cardiovascular disease and cancer. Therefore, both monitoring and managing of stress is imperative to reduce the bad outcomes from excessive psychological stress. Conventional monitoring methods firstly extract the characteristics of the RR interval of an electrocardiogram (ECG) from a time domain and a frequency domain, then use machine learning models, like SVM, random forest, and decision tree, to distinguish the level of that stress. The biggest limitation of using these methods is that at least one minute of ECG data and other signals are indispensable to ensure the high accuracy of the results. This will greatly affect the real-time application of the models. To satisfy real-time detection of stress with high accuracy, we proposed a framework based on deep learning technology. The proposed monitoring framework is based on convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM). To evaluate the performance of this network, we conducted the experiments applying conventional methods. The data for the 34 subjects were collected on the server platform created by the group at the Institute of Psychology of the Chinese Academy of Sciences and our group. The accuracy of the proposed framework was up to 0.865 on three levels of stress using a 10 s ECG signal, a 0.228 improvement compared with conventional methods. Therefore, our proposed framework is more suitable for real-time applications
Highlights
Economic development has led to fierce competition among people
MAE [4] and JITAI [5] are two effective methods that can be used to deal with the negative consequences of excessive psychological stress
Se-Hui Song [13] categorized stress into four classes, using SBP, DBP, sleep time, heart rate, and age with DBN and the accuracy only reached 0.66, and the data sleep time, SBP, DBP used did not satisfy a real-time application well. These studies have proven their models to be effective, they did not consider the need for real-time application. To address this issue, considering the effectiveness of deep learning technology and the easy accessibility of ECG signals, we proposed a network according to ECG using deep learning technology
Summary
Economic development has led to fierce competition among people. People are more prone to be affected by high psychological stress. Excessive psychological stress reduces work efficiency, affects relationships and transportation safety [1]. Long-term stress can even induce depression, addiction, and cardiovascular and cerebrovascular diseases [2,3]. Excessive emotional stress has become a major problem affecting human physical and mental health. MAE (ecological momentary assessment) [4] and JITAI (just-intime adaptive interventions) [5] are two effective methods that can be used to deal with the negative consequences of excessive psychological stress. The two methods both require real-time monitoring of psychological stress, and the lack of a method that can monitor emotional stress in real time has become the main problem
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