A Physiological Signal Analysis Algorithm for Human Stress Recognition from ECG Signal

Our work's primary goal is to process electrocardiogram data utilizing the Fission-Fusion method of the Hilbert spectrum. One of the most effective techniques for the analysis of nonlinear and non-stationary signals is based on the Hilbert Huang Transform (HHT). We have processed Electrocardiograms (ECGs) by applying EMD after the Wavelet Packet Transform since our method is better appropriate for nonlinear and non-stationary signals (WPT). In order to produce mono-component, breakdown the ECG signal into a number of narrow band signals, and remove unnecessary IMF, WPT is applied. In order to identify human emotions in our work, we employed the Fi-Fu algorithm to analyze electrocardiogram (ECG) signals for the detection of significant parameters such instantaneous frequency, amplitude, mean frequency, and second order difference plot. The EMD procedure has the ability to handle noise seen in ECG signals. Wavelet Packet Transform (WPT) is a more practical alternative to wavelet transform in real-world scenarios including signal analysis and denoising. WPT possesses unusual localization and enhanced discerning capabilities in the high frequency domain. WPT separates the high-frequency and low- frequency components of the frequency information of the signals to be studied.