Investigating Windowing Techniques in Emotion Classification with ECG and Machine Learning

Abstract

Automated emotion recognition using physiological signals, particularly Electrocardiogram (ECG), has diverse applications in human-computer interaction, healthcare, and psychology. This study proposes a novel ECG-based emotion recognition approach, utilizing time-series to image encoding, texture-based features, and machine-learning algorithms. The Continuously Annotated Signals of Emotion dataset is used, and emotional states are categorized based on arousal and valence annotations. ECGs are segmented into 5 and 7-window segments and transformed into 2D representations using Markov Transition Field (MTF). Extracting 43 features from the Gray-Level Co-occurrence Matrix and Gray-Level Run Length Matrix (GLRLM), three classifiers, including Random Forest (RF), Support Vector Machine, and eXtreme Gradient Boosting (XGB), are employed for emotion classification. The 7-window approach yields superior results, achieving a peak accuracy of 76.69% with XGB. High-Valence Low-Arousal emotional states are recognized best, with the highest F-measure of 61.9%. The findings suggest the potential for accurate and efficient emotion recognition using ECG, MTF, and machine-learning classifiers. © 2023 IEEE.