A Hybrid Approach for Stress Prediction from Heart Rate Variability

Abstract

Stress is a condition that causes a specific physiologicsal response. Heart rate variability (HRV) is a critical aspect in identifying stress. It is crucial for those who want to keep track of their wellness. Currently, numerous research is being conducted on stress prediction from HRV. The existing works in this field cover different data sets to identify stress, where significantly few models can predict stress with high accuracy. This work combines two well-known stress prediction data sets comprising HRV features named WESAD and SWELL-KW to compare twelve classical machine learning models and hybrid models. Finally, it proposes a hybrid stress prediction model that combines Artificial Neural Network (ANN) with Naive Bayes (NB). The proposed model performed auspiciously, having an accuracy of 95.75% within only 0.80 s. A stress prediction framework is also suggested based on the findings.