Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics. Issue 3 (December 2015)

• Record Type: Journal Article
• Authors: Zheng, Yineng; Guo, Xingming; Qin, Jian; Xiao, Shouzhong
• Is Part Of: Computer methods and programs in biomedicine. Volume 122:Issue 3(2015)
• Page Start: 372
• Page End: 383
• Publication Date: 2015-12
• Subjects: Heart sound -- Cardiac reserve -- MF-DFA -- MESE -- EMD -- CHF; 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.2015.09.001
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We proposed an innovative LS-SVM based system for diagnosis of CHF. Two novel features of heart sound such as f PSD max and sub _ EF were proposed. The cardiac reserve indexes and heart sound features were used to diagnose the CHF. The classification performances of LS-SVM, HMM and BP-ANN were compared. Abstract: An innovative computer-assisted diagnosis system for chronic heart failure (CHF) was proposed in this study, based on cardiac reserve (CR) indexes extraction, heart sound hybrid characteristics extraction and intelligent diagnosis model definition. Firstly, the modified wavelet packet-based denoising method was applied to data pre-processing. Then, the CR indexes such as the ratio of diastolic to systolic duration ( D / S ) and the amplitude ratio of the first to second heart sound ( S 1/ S 2) were extracted. The feature set consisting of the heart sound characteristics such as multifractal spectrum parameters, the frequency corresponding to the maximum peak of the normalized PSD curve ( f PSD max ) and adaptive sub-band energy fraction ( sub _ EF ) were calculated based on multifractal detrended fluctuation analysis (MF-DFA), maximum entropy spectra estimation (MESE) and empirical mode decomposition (EMD). Statistical methods such as t -test and receiver operating characteristic (ROC) curve analysis were performed to analyze the difference of each parameter between the healthy and CHF patients. Finally, least square support vector machine (LS-SVM) was employed for the implementation of intelligent diagnosis. The result indicates the achieved diagnostic accuracy, sensitivity and specificity of the proposed system are 95.39%, 96.59% and 93.75% for the detection of CHF, respectively. The selected cutoff values of the diagnosis features are D / S = 1.59, S 1/ S 2 = 1.31, Δ α = 1.34 and f PSD max = 22.49, determined by ROC curve analysis. This study suggests the proposed methodology could provide a technical clue for the CHF point-of-care system design and be a supplement for CHF diagnosis.