Neurophysiological Feature Based Stress Classification Using Unsupervised Machine Learning Technique

Abstract

Mental stress is the primary concern of increasing mental health problems and other medical problems like strokes, heart attacks, and ulcers. Thus, identifying and classifying stress at an early age is the prime requirement to avoid such diseases. Although a number of studies focused to predict and classify stress based on neurophysiological (brain wave and heart rate) data and used the supervised machine learning technique, but a little attention has been paid to explore the performances of unsupervised learning techniques in stress prediction. In this article, an unsupervised machine learning approach is proposed to classify mental stress into three categories: acute (low stress), episodic acute (moderate stress), and severe (high stress). The K-means clustering algorithm was used in the proposed methodology to create three different clusters, which depicts the aforementioned stress levels. The goodness of the clustering technique was evaluated by Silhouette Coefficient, and a standard fitness score of 0.76 was achieved.