Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 24; p. 8130
• Main Authors: Mercado-Diaz, Luis R., Veeranki, Yedukondala Rao, Large, Edward W., Posada-Quintero, Hugo F.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.12.2024
• Subjects: Adult; Algorithms; Arousal - physiology; Datasets; detrended fluctuation analysis; electrodermal activity; emotional states; Emotions; Emotions - physiology; Entropy; Female; Fourier transforms; fractal analysis; Fractals; Galvanic Skin Response - physiology; Humans; Machine Learning; Male; Mental health; Nervous system; Physiology; Signal processing; Signal Processing, Computer-Assisted; Time series; Trends; Wavelet Analysis; Wavelet transforms; Young Adult; electrodermal activity; fractal analysis; machine learning; emotional states; detrended fluctuation analysis
• DOI: 10.3390/s24248130
• ISSN: 1424-8220

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human–computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants. The analysis revealed significant differences in fractal features across five emotional states (neutral, amused, bored, relaxed, and scared), particularly those derived from wavelet entropy. A cross-correlation analysis showed robust correlations between fractal features and both the arousal and valence dimensions of emotion, challenging the conventional view of EDA as a predominantly arousal-indicating measure. The application of machine learning for emotion classification using fractal features achieved a leave-one-subject-out accuracy of 84.3% and an F1 score of 0.802, surpassing the performance of previous methods on the same dataset. This study demonstrates the potential of fractal analysis in capturing the intricate, multi-scale dynamics of EDA signals for emotion recognition, opening new avenues for advancing emotion-aware systems and affective computing applications.