Mercado-Diaz Luis R, Veeranki Yedukondala Rao, Large Edward W, Posada-Quintero Hugo F
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Dharwad, Dharwad 580009, India.
Sensors (Basel). 2024 Dec 19;24(24):8130. doi: 10.3390/s24248130.
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.
从生理信号中进行情感识别的领域是一个不断发展的研究领域，对心理健康监测和人机交互都具有重要意义。本研究介绍了一种基于皮肤电活动（EDA）信号分形分析来检测情绪状态的新方法。我们采用去趋势波动分析（DFA）、赫斯特指数估计和小波熵计算，从CASE数据集中获取的EDA信号中提取分形特征，该数据集包含30名参与者的生理记录和连续的情绪注释。分析揭示了五种情绪状态（中性、愉悦、无聊、放松和恐惧）之间分形特征的显著差异，特别是那些来自小波熵的特征。互相关分析表明，分形特征与情绪的唤醒度和效价维度之间存在强相关性，这对将EDA主要视为唤醒指示指标的传统观点提出了挑战。使用分形特征进行情感分类的机器学习应用实现了留一法准确率84.3%和F1分数0.802，超过了之前在同一数据集上的方法的性能。本研究证明了分形分析在捕捉EDA信号复杂的多尺度动态以进行情感识别方面的潜力，为推进情感感知系统和情感计算应用开辟了新途径。
