title: Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.
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 2s 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 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore. Electronic address: vidya.2kus@gmail.com.
authors.forename: Vidya K
authors.initials: VK
authors.lastname: Sudarshan
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
authors.forename: Shu Lih
authors.initials: SL
authors.lastname: Oh
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Adam
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
authors.forename: Jen Hong
authors.initials: JH
authors.lastname: Tan
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
authors.forename: Chua Kuang
authors.initials: CK
authors.lastname: Chua
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
authors.forename: Kok Poo
authors.initials: KP
authors.lastname: Chua
authors.affiliation: Department of Cardiology, National Heart Centre, Singapore.
authors.forename: Ru San
authors.initials: RS
authors.lastname: Tan
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006333:Heart Failure / Q000175:diagnosis*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis; D055815:Young Adult
keywords: Congestive heart failure; Decision tree; Dual tree complex wavelet transform; Electrocardiogram; K-nearest neighbor; Statistical features