Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme. (May 2019)

• Record Type: Journal Article
• Authors: Tripathy, R.K.; Paternina, Mario R.A.; Arrieta, Juan G.; Zamora-Méndez, Alejandro; Naik, Ganesh R.
• Is Part Of: Computer methods and programs in biomedicine. Volume 173(2019)
• Page Start: 53
• Page End: 65
• Publication Date: 2019-05
• Subjects: Congestive heart failure -- ECG -- Stockwell transform -- Time-Frequency entropy -- Hybrid classifier -- Performance evaluation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.03.008
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : highlights: A novel approachfor the detection of congestive heart failure from ECG signal is proposed. The time-frequency entropy features are evaluated. The combination of sparse representation classifier and the average of the distances of nearest neighbors is used. The proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48% and 99.09%, respectively. Abstract: Background and Objective: The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF. Methods: The approach is founded on the use of the Stockwell ( S )-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases. Results: The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished. Conclusions: The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems.