Electrocardiogram-based Emotion Recognition using Convolutional Neural Networks

Introduction: Electrocardiogram-based facial emotion identification has been a widely spread field in the last few decades. Due to its non-linearity, non-stationary and noisy properties, it is a very difficult job to create a framework that is capable of recognizing emotions with a high recognition rate. background: Electrocardiogram based facial emotion identification has been widely spread field in last some decades. Due to its non-linearity, non-stationary and noisy properties, it is very difficult job to create a framework that is capable of recognizing emotions with high recognition rate. Method: In this work, we introduce a new framework for facial emotion detection based on feature creation using a topographic representation of ECG signal properties. The feature map is created using deep learning techniques, and further, extricated features are then used for classification techniques to detect facial emotion recognition. Result: The recognition results are achieved on two publicly available facial expression datasets, i.e., Ascertain and Dreamer. We illustrated the usefulness of our framework by comparing results with other existing methods. Conclusion: The recognition results prove that the introduced framework can enhance the identifying rate on various given datasets.