Mental stress detection from ultra-short heart rate variability using explainable graph convolutional network with network pruning and quantisation

This study introduces a novel pruning approach based on explainable graph convolutional networks, strategically amalgamating pruning and quantisation, aimed to tackle the complexities associated with existing machine learning and deep learning models for stress detection using ultra-short heart rate variability analysis. These complexities often impede the implementation ability of such models on resource-limited devices. The proposed method exhibits exceptional performance, demonstrating high accuracy (97.75%) and efficiency (97.66%) on the WESAD dataset, along with an impressive accuracy (94.48%) and efficiency (94.39%) on the SWELL dataset. Importantly, the runtime complexity saw a significant reduction, down by 63.4% and 69.34% compared to the original model. The proposed method's notable advantage lies in its ability to retain nearly all of the initial model's performance with negligible loss, even when the pruning levels are below 60%. This innovative approach, thus, offers a promising solution for effective stress detection, specifically designed to operate smoothly on devices with limited resources.