A new data augmentation convolutional neural network for human emotion recognition based on ECG signals

Nowadays, human emotion recognition based on electrocardiogram (ECG) signal is considered as a hot topic applied in many sensitive domains such as healthcare, social security, and transportation systems. In the literature, various machine learning algorithms were proposed to this purpose however, the recognition accuracy of these techniques is hampered by the hardness of acquiring huge and balanced number of ECG dataset samples, which is considered as a major challenge in this topic. Therefore, we propose in this paper, a new data augmentation convolutional neural network (CNN) for human emotion recognition based on ECG signal. Specifically, we suggest to enrich the ECG dataset by a significant number of representative ECG samples, generated according to randomize, concatenate and resample realistic ECG episodes process. Hence, a new seven-layer CNN classifier is suggested, consisting of seven layers to detect human emotions in terms of valence, arousal, and dominance levels. Experiments that have been carried out using our proposal for Data Augmentation Convolutional Neural Network strategy on benchmark DREAMER database resulted in an accuracy rate of 95.16% to detect valence, 85.56% for arousal and 77.54% for dominance.

Automated summary

In this paper, a new data augmentation convolutional neural network (CNN) for human emotion recognition based on ECG signal is proposed. By enriching the ECG dataset and using a seven-layer CNN classifier, high accuracy rates were achieved for valence, arousal, and dominance detection.