Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal

TL;DR

This paper proposes a multi-level stress assessment method using multi-domain fusion of ECG signals transformed into images, employing deep learning to classify stress levels with high accuracy.

Contribution

It introduces a novel multi-domain fusion approach with ECG signal image conversion and deep learning for multi-level stress classification, surpassing binary assessments.

Findings

Achieved 85.45% average classification accuracy.

Utilized multimodal ECG signal images in time-frequency and frequency domains.

Demonstrated the effectiveness of decision-level fusion in stress level classification.

Abstract

Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a burning research topic in biomedical signal processing. However, existing literature provides only binary assessment of stress, while multiple levels of assessment may be more beneficial for healthcare applications. Furthermore, in present research, ECG signal for stress analysis is examined independently in spatial domain or in transform domains but the advantage of fusing these domains has not been fully utilized. To get the maximum advantage of fusing diferent domains, we introduce a dataset with multiple stress levels and then classify these levels using a novel deep learning approach by converting ECG signal into signal images based on R-R peaks without any feature extraction. Moreover, We made signal images multimodal and multidomain by converting them into time-frequency and frequency domain using Gabor wavelet transform (GWT) and Discrete Fourier Transform (DFT) respectively. Convolutional Neural networks (CNNs) are used to extract features from different modalities and then decision level fusion is performed for improving the classification accuracy. The experimental results on an in-house dataset collected with 15 users show that with proposed fusion framework and using ECG signal to image conversion, we reach an average accuracy of 85.45%.