Stress-based Classification of Electrocardiogram Signals Before and After Music Therapy using Heart Rate Variability and Machine Learning

Abstract

The harmful impacts of excessive stress on people’s health have been widely acknowledged, necessitating effective methods for its identification. Recognizing the importance of early stress detection and intervention, this research aims to contribute to the field of healthcare. To achieve this objective, this study classifies electrocardiogram (ECG) signals by assessing physio-psychological states, specifically stress and examines the role of music therapy in alleviating stress. ECG signals, recorded both before and after a music therapy session, were collected. Using signal processing techniques, essential features were extracted from these ECG signals, resulting in a more accurate identification of stress. Additionally, through experimentation and model evaluation, k-nearest Neighbors (KNN) and Classification and Regression Trees (CART) were determined to be the most effective models for this classification. Both models consistently yielded 90% accuracy. These identified extracted features and models are vital to effectively recognizing stress in ECG signals, offering valuable insights for future studies and clinical applications. This research contributes not only to the development of tools for stress detection but also to the understanding of the therapeutic impact of music.