Combined empirical mode decomposition and phase space reconstruction based psychologically stressed and non-stressed state classification from cardiac sound signals

Abstract

The psychological stress and associated mental health conditions causes high socioeconomic impacts on society and the onset of pandemic worsened the situation making it imperative to timely detect psychological stress. This paper presents a novel framework for using a combination of empirical mode decomposition (EMD) and phase space reconstruction (PSR) analysis to capture non-linear, non-stationary dynamics of cardiac sound signals acquired from fifty-four healthy male adults for detecting psychological stress. The time interval among successive S1 peaks of acquired cardiac sound signals is extracted to obtain Interbeat Interval signal used for decomposition to Intrinsic Mode Functions (IMFs) using EMD technique. Thereafter, the feature vectors namely- largest singular value, smallest singular value from two-dimensional PSRs of IMFs and the mean value of Euclidean distance, standard deviation of Euclidean distance from three-dimensional PSRs are extracted to detect psychologically stressed state. The non-parametric Kruskal-Wallis statistical test is applied to select statistically significant features that are fed to Decision Tree, Naïve Bayes and Support Vector Machine classifiers for classifying stressed and non-stressed state with fivefold cross-validation to make it a reliable system. The average accuracy, sensitivity and specificity achieved is 97.14%, 99.8% and 94% respectively using SVM and Radial Basis Function kernel function. The proposed framework performed better on the dataset in comparison to ratio of low-frequency to high-frequency (LF/HF) power parameter of Electrocardiography signal. The use of easy to acquire, cost-effective cardiac sound signals for detecting psychological stress makes proposed framework feasible for rural healthcare centres of developing economies, homecare and telemedicine.