Key result
7-window ECG segmentation with XGBoost achieves ~77% peak emotion classification accuracy.
Why the study?
Automated emotion recognition using physiological signals such as ECG has diverse applications, motivating novel approaches utilizing time-series to image encoding, texture-based features, and machine learning.
Machine learning algorithms applied to ECG signals transformed into 2D images can classify emotional states with up to 76.69% accuracy.
Supports ML feasibility for ECG-based emotion detection; leaves open larger clinical validation before adoption.
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.
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Govarthan et al. (2023) studied Emotion recognition. 7-window ECG segmentation with MTF and XGBoost classifier vs. 5-window approach and other classifiers (RF, SVM) was evaluated on Emotion classification accuracy. A 7-window ECG segmentation approach using Markov Transition Field and eXtreme Gradient Boosting achieved a peak emotion classification accuracy of 76.69%.
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