  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%.
