Dimension-raising Processing Framework for One-dimensional Time Series and its Application in Affect Detection

This paper explores the application of neural networks in the affect detection from Electrocardiograph(ECG) data. Affect recognition is one of the most challenging tasks. Because of the great cultural and personalized differences in the image and sound-based affect detection, and the physiological signal response of emotion is more universal and accurate, we detect emotions from physiological signals. This paper proposed a detection framework for single-modal physiological signals. This work detects affect from nonstationary ECG data. In this work, we use ECG data from the dataset from UCI named Multimodal Dataset for wearable Stress and Affect Detection(WESAD), include ECG signals from 15 Subjects. To extract features from the ECG data, We propose a stacking operation to increase ECG data's dimension. with this operation, we use a convolutional neural network (CNN) to extract multiscale periodical features of ECG data easily. Experimental results show that the stack based VGG can capable of classifying four and five different kinds of affect with an accuracy of 97.78% and 95.87% respectively. The high-dimensional convolutional neural network provides better performance compared to one-dimensional convolutional neural network models. This approach can also be applied to other applications of single-modal physiological signals.