Psychological stress detection using phonocardiography signal: An empirical mode decomposition approach

• Published in: Biomedical signal processing and control Vol. 49; pp. 493 - 505
• Main Authors: Cheema, Amandeep, Singh, Mandeep
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2019
• Subjects: Empirical mode decomposition; Phonocardiography; Psychological stress; Psychological stress; Empirical mode decomposition; Phonocardiography
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2018.12.028

•ECG and PCG signals are temporally correlated, PCG signal used as an alternative to ECG for psychological stress detection.•The use of subject-specific template analysis to cater to characteristic cardiac behaviour of every individual.•The statistical significance analysis to find features that are significant across subjects.•The comparative analysis identity features that are valuable for both subject-specific analysis and analysis across subjects.•The proposed method performed better than ECG-based LF/HF power ratio method on the available dataset. Psychological stress is a part of the modern day lifestyle and affects human cognitive abilities. The well-established relation between stress and a host of behavioural and somatic pathological conditions emphasizes the need for timely detection of psychological stress. The purpose of this research work is to present a novel framework for psychological stress detection using Phonocardiography (PCG) signal based on Empirical Mode Decomposition (EMD) technique. The methods like Electroencephalography (EEG) and Electrocardiography (ECG) provide important biophysical measures for psychological stress detection but are expensive or require a proper clinical setup. Whereas, the acoustic heart sound or PCG signals carry significant information and can be easily acquired. In this research, pre-competitive (or exam related) psychological stress is detected from the S1-S1 interval of PCG signal referred as Inter-beat Interval (IBI). The IBI signal is decomposed to Intrinsic Mode Functions (IMF) using EMD technique which is suitable for non-linear and non-stationary signal analysis. The non-linear features namely Area of Analytic Signal Representation (AASR), Log of Area of ellipse from Second-order Difference Plot (LASODP), Root Mean Square value of IMF (RmsIMF), Shannon Entropy (ShEnt) and Fuzzy Entropy (FzEnt) were evaluated from IMFs of IBI signals. The first set of experiments comprises of deviation analysis in stressed signals from mean baseline values of the features in non-stressed signals. Thereafter, in the second set of experiments, Kruskal-Wallis statistical test has been used to check the significance and discrimination ability of the features. Then the features which showed maximum deviation and are statistically significant have been selected and fed to least-square support vector machine (LS-SVM) classifier. The 10-fold cross-validation has been used to make the system more reliable and robust. In this work, the average accuracy of 93.14% in classifying stressed and non-stressed signals has been achieved using Radial Basis Function (RBF) kernel. The results indicate that the proposed features provide better discrimination ability than well-known low-frequency to high-frequency power ratio (LF/HF) parameter of the ECG signal. The novelty of this study is the use of PCG signals for psychological stress detection and the use of subject-specific baseline template to incorporate the individual cardiovascular characteristic behaviour and stress responses. The proposed novel methodology of using PCG signals for psychological stress detection is cost-effective and is suitable for home-care, telemedicine and in rural health care centres especially in developing countries.