Stress Detection from Multimodal Physiological Data Using Hybrid Deep Learning Models

Chronic stress and depression-adjacent emotional states affect over 970 million people worldwide, yet their continuous, objective detection from physiological signals remains unsolved, particularly when cues are subtle and conventional approaches fail. Single modality classifiers capture only part of the picture, and binary valence/arousal formulations collapse emotionally distinct states into the same category, leaving conditions like sadness and depression characterised by Low-Valence Low-Arousal (LVLA) responses without reliable detection. A hybrid deep learning model combining Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer encoders was developed to jointly classify four emotional quadrants from multimodal physiological data in the Database for Emotion Analysis using Physiological signals (DEAP) dataset. Electroencephalography (EEG), Galvanic Skin Response (GSR), Blood Volume Pulse (BVP), and respiration signals from 32 participants were preprocessed using fourth-order Butterworth filtering, trained with Adam optimisation, and evaluated through an 80/20 stratified split with five-fold cross-validation. The system achieved 91.2% four-quadrant valence–arousal accuracy (95% CI: 89.1–93.3%), with LVLA recall reaching 91.3%, outperforming all partial-hybrid variants. These findings demonstrate that hierarchical, attention-based fusion of physiological modalities can reliably distinguish stress from depression-adjacent states, offering a practical pathway toward continuous, non-invasive mental health monitoring on wearable platforms.