ECG based Categorical emotion classification using time-domain features and Machine Learning

Emotions are mental states that result from neurophysiological changes associated with thoughts, feelings, and behavioral responses. Emotions lead to modifications in heart rate variability, which can be identified through electrocardiogram (ECG) signals. In this study, we attempted to analyze the ECG signals to detect categorical emotions using time-domain features and machine-learning algorithms. Initially, the ECG signals of 30 subjects were obtained from the publicly available continuously annotated signals of emotion dataset. Further, the signals were preprocessed and extracted 32-time domain features from ECG signals which were recorded during different emotional states such as amusing, boring, relaxing, and scary. The extracted features were fed to a random forest (RF) classifier to rank the features and to build the three machine learning models such as logistic regression (LR), support vector machine, and RF. We achieved the highest average classification accuracy, sensitivity, specificity, precision, and f1-score of 71.04%, 42.08%, 80.69%, 43.03%, and 42.32%, respectively, with the top 4 features using the LR classifier. We found that the mean of peaks, slope sign change, dynamic range, and mean of first derivative were ranked top and played a significant role in the classification model. Our study shows the effectiveness of utilizing ECG signals for emotion detection in wearable devices