Stress Detection and Prediction Using CNNs from Electrocardiogram Signals

Jehad Ur Rahman, Samad Riaz, Salma ()
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Jehad Ur Rahman, Samad Riaz, Salma: Department of Electrical Engineering UET Peshawar, Pakistan. Institute of Nursing Science KMU Peshawar, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 5, 207-215

Abstract: Stress prediction is a crucial aspect of mental health monitoring, with consequences for both psychological well- being and productivity. This work presents a unique way for stress prediction that uses binary and multiclass classification models. Through extensive experimentations with different durations and frequencies of Electrocardiogram Signal (ECG) signals, we identified a 5-second dataset sampled at 200Hz as the optimal configuration for our model. Moreover, we introduced an innovative feature i.e., the prediction of stress scores ranging from 0 to 100, providing nuanced insights into stress levels, where 0 represents no stress and 100 indicates high stress levels. The model obtains 95.04% accuracy, 95.27% precision, 94.95% F1 score, 86.69% sensitivity, and 99.44% specificity for the binary classification. With "Fun" added to the list of stress categories in addition to "Base" and "TSST," the model continues to perform well in the multiclass classification scenario, with accuracy of 88.10%, precision of 87.60%, F1 score of 87.35%, sensitivity of 95.97%, and specificity of 79.23%. These findings highlight how well this applied strategy predicts stress levels, providing important information for mental health and stress management strategies.

Keywords: Stress Detection; Stress Score; ECG; WESAD; ECG Signals; Stress Levels; CNNs (search for similar items in EconPapers)
Date: 2024
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