**6. Conclusions**

This paper proposes a deep psychological stress classification method based on ECG signals. First, HRV feature samples containing the timing information of ECG signals are constructed. Deep GRU networks are then used to extract deep features from HRV feature samples that have more essential and general connections to psychological stress states. Finally, a multi-layer, fully connected network is used to fuse the deep and shallow features of the GRU network to predict the psychological stress state. The experimental results show that the proposed method is a robust psychological stress estimation scheme, and its estimation accuracy in this dataset is 0.78 better than other mainstream methods.

However, we noticed that the classification accuracy is not very high. In future work, we will try to further improve the accuracy of psychological stress classification from the following aspects. The first is that the amount of information input to the classification model can be increased by introducing other physiological signals besides ECG, such as

EEG and EDA, or extracting more valuable features from ECG signals, thereby improving the performance of stress classification. Secondly, we can also consider reducing the differences in physiological signals between individuals to improve the classification accuracy of psychological stress. Specifically, domain adaptation methods in transfer learning have achieved good results in many image datasets with large distribution differences, and in recent years, this method has achieved high performance in EEG-based cross-subject emotion recognition accuracy [42,43]. Therefore, we will consider introducing a transfer learning method to further improve the classification accuracy of psychological stress states. Furthermore, high-level feature design and feature space applicable reduction to multidimensional wearable sensors, such as referable approaches for wearable-based HAR, are also worthy of further experimentation [14,44].

**Author Contributions:** All of the authors made significant contributions to this work. J.Z. and D.H. designed the psychological stress experiment, developed the algorithm and wrote the manuscript. Y.L. and L.C. developed the ECG collection equipment and organized the experiment. X.C. and W.C. developed the VR equipment and experimental scenarios. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Key Deployment Project of the Chinese Academy of Sciences (Psychophysiological Intelligence Monitoring Technology for Small Group Target Figures, No. KGFZD-145-21-09-01) and Jiangsu Province Industrial Foresight and the Key Core Technology Project (Research and Development of Key Technologies for Demonstration and Application of Multi-scenario Brain-Computer Interaction System for Smart Elderly Care, No. BE2022064-2).

**Institutional Review Board Statement:** The research was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board the Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data are not publicly available due to the relevant project regulations.

**Conflicts of Interest:** The authors declare no conflict of interest.
