Multi-Class Stress Detection through Heart Rate Variability: A Deep Neural Network-Based Study
DOI:
https://doi.org/10.69996/jsihs.2025013Keywords:
Heart Rate Variability (HRV), Stress Detection, Convolutional Neural Network (CNN), Deep Learning, Multiclass Classification, Physiological Signals, Mental Health Monitoring, Electrocardiogram (ECG), Artificial Intelligence, Biomedical Signal Processing, Affective Computing, Wearable Devices, HRV Features, Stress Prediction, Neural NetworksAbstract
Stress is a natural physiological and psychological response to external demands or perceived threats. However, when stress becomes chronic or excessive, it significantly heightens the risk of mental and physical health disorders, including anxiety, depression, and sleep disturbances. A dependable physiological indicator for stress evaluation is heart rate variability (HRV), which shows the fluctuation in the time intervals between subsequent heartbeats (RR intervals). Heart rate variability (HRV) records the dynamic changes in cardiac activity, providing greater insights into autonomic nervous system function, as opposed to heart rate, which offers an average beats-per-minute assessment. Here, we present a CNN-based deep learning model that investigates the possibility of HRV features as biomarkers for multi-class stress detection. A lack of stress, stress caused by interruptions, and stress caused by time pressure are the three types of stress that the model is intended to classify. In order to improve the accuracy of detection, HRV is used in both the time-domain and frequency-domain. The suggested model is tested on the open-source SWELL-KW dataset and shows remarkable results: a 99.9% accuracy rate, a 1.0 precision score, a 1.0 recall score, a 1.0 F1-score, and a 0.99 Matthews Correlation Coefficient (MCC). In addition, an analysis of variance (ANOVA) feature extraction method validates the efficacy of critical HRV features. The results show that deep learning methods based on HRV are capable of reliable multi-class stress identification.
References
[1] J.A. Mortensen, M.E. Mollov, A. Chatterjee, D. Ghose and F.Y. Li, “Multi-class stress detection through heart rate variability: A deep neural network based study,” IEEE Access, vol.11, pp.57470- 57480, 2023.
[2] C. S. Kumar, O. Bhaskaru, B. Karanam, A.A. Ahmed, Y.G.C.T. Reddy and M. Sathwik, “Exploring multi-class stress detection using deep neural networks,” In 2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS), pp. 69-74, 2024.
[3] G.Singh, O.C. Phukan and R. Kumar, “Stress recognition with multi‐modal sensing using bootstrapped ensemble deep learning model,” Expert Systems, vol.40, no.6, pp.e13239, 2023.
[4] A. Hemakom, D. Atiwiwat and P. Israsena, “ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study,” Plos one, vol.18, no.9, pp.e0291070, 2023.
[5] M. Benchekroun, P.E. Velmovitsky, D. Istrate, V. Zalc, P.P. Morita and D. Lenne, “Cross dataset analysis for generalizability of HRV-based stress detection models,” Sensors, vol.23, no.4, pp.1807, 2023.
[6] R. Kuttala, R. Subramanian and V.R.M. Oruganti, “Multimodal hierarchical CNN feature fusion for stress detection,” IEEe Access, vol.11, pp.6867-6878, 2023.
[7] B. Leela Vathy and RamMohan Rao Kovvur, “Deep Convolutional Neural Network (DEEP-CNN) for Multi-Class Classification of Biotic Stress in Paddy Crop,” Journal of Computer Allied Intelligence, vol.3, no.1, pp.18-30, 2025.
[8] Y. Haque, R.S. Zawad, C.S.A. Rony, H. Al Banna, T. Ghosh, M.S. Kaiser and M. Mahmud, “Stateof-the-art of stress prediction from heart rate variability using artificial intelligence,” Cognitive Computation, vol.16, no.2, pp.455-481, 2024.
[9] N. Rashid, T. Mortlock and M.A. Al Faruque, “Stress detection using context-aware sensor fusion from wearable devices,” IEEE Internet of Things Journal, vol.10, no.16, pp.14114-14127, 2023.
[10] P. Kalra and V. Sharma, “Mental stress assessment using PPG signal a deep neural network approach,” IETE Journal of Research, vol.69, no.2, pp.879-885, 2023.
[11] T. Bhaskar, M.N. Narsaiah and M. Ravikanth, “Central Medical Centre Healthcare Data Security with Lightweight Blockchain Model in IoT Sensor Environment,” Journal of Sensors, IoT and Health Sciences, vol.1, no.1, pp.15-26, 2023.
[12] G. Dogan and F.P. Akbulut, “Multi-modal fusion learning through biosignal, audio, and visual content for detection of mental stress,” Neural Computing and Applications, vol.35, no.34, pp.24435-24454, 2023.
[13] Kristy Anjala, “Understanding the Academic Stress: Factors, Impact and Strategies: A Review,” International Journal of Advance Research and Innovation, vol.12, no.1, pp.11-16, 2024.
[14] J.S. Banerjee, M. Mahmud and D. Brown, “Heart rate variability-based mental stress detection: an explainable machine learning approach,” SN Computer Science, vol.4, no.2, pp.176, 2023.
[15] G. Rescio, A. Manni, M. Ciccarelli, A. Papetti, A. Caroppo and A. Leone, “A deep learning-based platform for workers’ stress detection using minimally intrusive multisensory devices,” Sensors, vol.24, no.3, pp.947, 2024.
[16] Z. Ahmad, S. Rabbani, M.R. Zafar, S. Ishaque, S. Krishnan and N. Khan, “Multilevel stress assessment from ECG in a virtual reality environment using multimodal fusion,” IEEE Sensors Journal, vol.23, no.23, pp.29559-29570, 2023.
[17] T. Fan, S. Qiu, Z. Wang, H. Zhao, J. Jiang et al., “A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition,” Computers in Biology and Medicine, vol.159, pp.106938, 2023.
[18] R. Doroudi, S.H.H Lavassani and M. Shahrouzi, “Optimal tuning of three deep learning methods with signal processing and anomaly detection for multi-class damage detection of a large-scale bridge,” Structural Health Monitoring, vol.23, no.5, pp.3227-3252, 2024.
[19] E.K. Naeini, F. Sarhaddi, I. Azimi, P. Liljeberg, N. Dutt and A.M. Rahmani, “A deep learning– based PPG quality assessment approach for heart rate and heart rate variability,” ACM Transactions on Computing for Healthcare, vol.4, no.4, pp.1-22, 2023.
[20] P.N. Hadinata, D. Simanta, L. Eddy and K. Nagai, “Multiclass segmentation of concrete surface damages using U-net and DeepLabV3+,” Applied Sciences, vol.13, no.4, pp.2398, 2023.
[21] Y. Li, K. Li, J. Chen, S.Wang, H. Lu and D.Wen, “Pilot stress detection through physiological signals using a transformer-based deep learning model,” IEEE sensors journal, vol.23, no.11, pp.11774-11784, 2023.
[22] D. Garg, G.K. Verma and A.K. Singh, “EEG-based emotion recognition using MobileNet Recurrent Neural Network with time-frequency features,” Applied Soft Computing, vol.154, pp.111338, 2024.
[23] A. D. Goswami, G. S. Bhavekar and P.V. Chafle, “Electrocardiogram signal classification using VGGNet: a neural network based classification model,” International Journal of Information Technology, vol.15, no.1, pp.119-128, 2023.
[24] M.T. Nguyen, W.W. Lin and J.H. Huang, “Heart sound classification using deep learning techniques based on log-mel spectrogram,” Circuits, Systems, and Signal Processing, vol.42, no.1, pp.344-360, 2023.
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