Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals. (July 2018)

• Record Type: Journal Article
• Authors: Adam, Muhammad; Oh, Shu Lih; Sudarshan, Vidya K; Koh, Joel EW; Hagiwara, Yuki; Tan, Jen Hong; Tan, Ru San; Acharya, U Rajendra
• Is Part Of: Computer methods and programs in biomedicine. Volume 161(2018)
• Page Start: 133
• Page End: 143
• Publication Date: 2018-07
• Subjects: Cardiovascular disease -- Dilated cardiomyopathy -- Hypertrophic cardiomyopathy -- Myocardial infarction -- Electrocardiogram -- Discrete wavelet transform; 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.2018.04.018
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Classification of normal, DCM, HCM and MI ECG signals. Four seconds of ECG segments are used. Nonlinear features are extracted from DWT decomposition. Feature selection is done using SFS and ReliefF method. Obtained accuracy of 99.27% using 15 features with kNN classifier. Abstract: Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy ( acc ) of 99.27%, sensitivity ( sen ) of 99.74%, and specificity ( spec ) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs. Graphical abstract: