Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals. (1st April 2017)

• Record Type: Journal Article
• Authors: Sudarshan, Vidya K.; Acharya, U.Rajendra; Oh, Shu Lih; Adam, Muhammad; Tan, Jen Hong; Chua, Chua Kuang; Chua, Kok Poo; Tan, Ru San
• Is Part Of: Computers in biology and medicine. Volume 83(2017)
• Page Start: 48
• Page End: 58
• Publication Date: 2017-04-01
• Subjects: Congestive heart failure -- Electrocardiogram -- Dual tree complex wavelet transform -- Statistical features -- K-nearest neighbor -- Decision tree; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.01.019
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2 s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t -test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2 s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments. Highlights: Identification of CHF using 2 s electrocardiogram signals is proposed. Features are extracted from dual tree complex wavelet transform coefficients. Extracted features are ranked using different ranking methods. Achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity using KNN classifier.