A new emotion detection algorithm using extracted features of the different time-series generated from ST intervals Poincare map

Emotion recognition using biological signals plays an essential role in studying psychological states. In many studies, the distinct effect of each physiological signal usually is ignored. By limiting the physiological signals and just evaluating the ECG signals, the paper aims to study the dynamical behavior of the Poincare map in the form of the feature extraction from the generated time-series. For this purpose, the detection of emotional states in the two-dimensional model of emotion (Arousal-Valence) is utilized using the electrocardiogram signals recorded on the MAHNOB-HCl tagging database. So after signal processing, the waves of Q, R, S, and T are detected by processing the ECG signals using the Pan-Tompkins algorithm. The Poincare map is adopted for the RR, QT, and ST intervals, then five different time-series are extracted from this mapping, subsequently, various features are extracted from these different time-series in the time domain, frequency domain, time-frequency domain, and nonlinear domain analysis. Finally, the classification of emotional states is performed using three classifiers: KNN, SVM, and MLP. In this study, the extracted optimal features of the different time-series generated from Poincare map of the ST Intervals are achieved the best average accuracies of 82.17 % +/- 4.73 and 78.07 % +/- 3.59 in the arousal and valence model, respectively. The superiority of the obtained results from the extracted features of the five different time-series generated from ST-Intervals Poincare map expresses that in comparison with RR and QT Intervals, the ST Intervals are more affected by ANS response to the emotional stimuli. (C) 2020 Elsevier Ltd. All rights reserved.