title: Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics.
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 (S1/S2) 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 (fPSDmax) 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, S1/S2=1.31, Δα=1.34 and fPSDmax=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.
authors.affiliation: Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, PR China.
authors.forename: Yineng
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, PR China. Electronic address: guoxm@cqu.edu.cn.
authors.forename: Xingming
authors.initials: X
authors.lastname: Guo
authors.affiliation: Department of Cardiology, First Affiliated Hospital, Chongqing University of Medical Sciences, Chongqing 400044, PR China.
authors.forename: Jian
authors.initials: J
authors.lastname: Qin
authors.affiliation: Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, PR China.
authors.forename: Shouzhong
authors.initials: S
authors.lastname: Xiao
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002908:Chronic Disease; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006333:Heart Failure / Q000175:diagnosis*; D006347:Heart Sounds / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D013312:Stress, Physiological / Q000502:physiology; D060388:Support Vector Machine; D055815:Young Adult
keywords: CHF; Cardiac reserve; EMD; Heart sound; MESE; MF-DFA