ECG - Based Emotion Detection via Parallel - Extraction of Temporal and Spatial Features Using Convolutional Neural Network

Emotion detection from an ECG signal allows the direct assessment of the inner state of a human. Because ECG signals contain nerve endings from the autonomic nervous system that controls the behavior of each emotion. Besides, emotion detection plays a vital role in the daily activities of human life, where we lately witnessed the outbreak of the (COVID-19) pandemic that has a bad influence on the affective states of humans. Therefore, it has become indispensable to build an intelligent system capable of predicting and classifying emotions in their early stages. Accordingly, in this study, the Parallel-Extraction of Temporal and Spatial Features using Convolutional Neural Network (PETSFCNN) is established. So, in-depth features of the ECG signals are extracted and captured from the suggested parallel 2-channel structure of 1-dimensional CNN network and 2-dimensional CNN network and then combined by feature fusion technique for more dependable classification results. Besides, Grid Search Optimized-Deep Neural Network (GSO-DNN) is adopted for higher classification accuracy. To verify the performance of the proposed method, our experiment was implemented on two different datasets. The maximum classification accuracy of 97.56% and 96.34% on both valence and arousal were gained, respectively using the internationally approved DREAMER dataset. While the same model on the private dataset achieved 76.19% for valence and 80.95% for arousal respectively. The classification results of the PETSFCNN-GSO-DNN model are compared with state-of-the-art methods. The empirical findings reveal that the proposed method can detect emotions from ECG signals more accurately and better than state-of-the-art methods and has the potential to be implemented as an intelligent system for affect detection.