Comparison of the representational ability in individual difference analysis using 2-D time-series image and time-series feature patterns

Abstract

Physiological signals, as crucial indicators for measuring physical health, must be acquired by model learning, but the modeling and analysis performance is strongly affected by different representation patterns and feature extraction methods, even for the same data. This work investigated the capacity of the 2-D time-series image patterns on the weakening of individual differences and compared them with the time-series feature patterns. First, the time-series features of electrocardiograph (ECG) were extracted using the permutation conditional mutual information (PCMI), principal component analysis (PCA), power spectral density (PSD), and compactly supported sym-5 orthogonal wavelets with approximate symmetry (CSOWAS); the time-series coupling features of electroencephalogram (EEG) and SEED were extracted using PCMI. Then, the features of ECG, EEG, and SEED were reconstructed into 2-dimensional (2-D) time-series images using Gradient Angle Difference Field (GADF) algorithm. Finally, the classification performance of the 2-D time-series images and time-series features on the ECG, EEG, and SEED data sets are analyzed using the backpropagation (BP) neural network, LeNet 5, convolutional neural network (CNN), Deep Residual Shrinkage Networks (DRSN), ResNet 18, and VGG 16 models and compared to each other. The results show that 1) the representation ability of the 2-D time-series image pattern is better than that of the time-series feature pattern in physiological data (ECG, EEG, and SEED) analysis; 2) the Gamma band is a stabler in the classification task of individual differences (best accuracy: 100%), which can be used as the first choice for the analysis of similarity features between individuals; 3) the classification performance of the 2-D time-series images in Theta, Alpha 1, Beta 1, and Beta 2 bands on EEG datasets are greatly improved. This work shows that the 2-D time-series image patterns of the ECG, EEG, and SEED are an effective method for distinguishing individual differences.