A Dual Path Hybrid Convolutional Neural Network and Bidirectional Long-Short Term Memory Approach for PPG-Based Stress Monitoring

Md Santo Ali, Mohammod Abdul Motin, Mufti Mahmud

Submission Track: Track 1: Machine Learning Research by Muslim Authors

Keywords: Convolutional neural network, LSTM, Bi-LSTM, photoplethysmography signal, stress monitoring.

TL;DR: A lightweight and accurate CNN-BiLSTM model using PPG signals for real-time stress detection for health monitoring.

Abstract: Mental stress adversely impacts both physical and mental health, with chronic stress leading to serious health concerns. Photoplethysmography (PPG) sensors, widely available in wearable devices, offer a convenient, cost-effective, and non-invasive method for stress monitoring. This study proposes a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid architecture for real-time stress detection using just PPG signals. Trained and validated on the publicly available WESAD dataset, the model achieves exceptional performance metrics: 97.90\% accuracy, 98.30\% specificity, 97.20\% sensitivity, 97.06\% F1-score, 99.12\% AUC, and 95.42\% Cohen's kappa. The lightweight model exhibits high accuracy in stress detection while maintaining computational efficiency, making it particularly suitable for wearable devices. These results highlight the potential of this approach for practical real-time stress monitoring and management applications.

Submission Number: 20