Evaluation of Novel Entropy-Based Complex Wavelet Sub-bands Measures of PPG in an Emotion Recognition System

• Published in: Journal of medical and biological engineering Vol. 40; no. 3; pp. 451 - 461
• Main Authors: Goshvarpour, Atefeh, Goshvarpour, Ateke
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2020; Springer Nature B.V
• Subjects: Affective computing; Biomedical Engineering and Bioengineering; Cell Biology; Classification; Emotion recognition; Emotions; Engineering; Entropy; Feature extraction; Imaging; Neural networks; Original Article; Permutations; Physiology; Radiology; Wavelet transforms; Dual tree complex wavelet transform; Emotion recognition; Probabilistic neural network; Entropy; Photoplethysmograph
• ISSN: 1609-0985; 2199-4757
• DOI: 10.1007/s40846-020-00526-7

Purpose Nowadays, the increasing demand for human–computer interface applications shows the social need to provide an accurate, intelligent emotion recognition system. Computer-aided emotion recognition using physiological signals remains a challenging task in “affective computing”. In this paper, the entropy-based complex wavelet sub-bands (ECWS) measures are suggested for the classification of 14 emotion categories from the photoplethysmograph (PPG). Methods PPG data available at DEAP (a Database for Emotion Analysis using Physiological signals) were selected when subjects were watching the fun, exciting, joy, happy, cheerful, love, sentimental, melancholy, sad, depressing, mellow, hate, shock, and terrible music videos. Using the dual-tree complex wavelet transforms, each PPG signal was decomposed into six levels. Four entropy measures, including approximate entropy, sample entropy, permutation entropy, and the improved multi-scale permutation entropy, were extracted from each sub-band coefficient. Then, the normalized ECWS features were input to the probabilistic neural network. The role of sigma adjustment was also considered in classifier performance. Results The results indicated the accuracy rates of 92 to 100% for the classification of 14 emotional states. The maximum accuracy rate was 100% for sigma < 0.25. Conclusion Our findings establish the proposed system as a superior framework compared to the state-of-the-art PPG emotion recognition tool.