title: Arrhythmia detection and classification using morphological and dynamic features of ECG signals.
abstract: Computer-assisted cardiac arrhythmia detection and classification can play a significant role in the management of cardiac disorders. In this paper, we propose a new approach for arrhythmia classification based on a combination of morphological and dynamic features. Wavelet Transform (WT) and Independent Component Analysis (ICA) are applied separately to each heartbeat to extract corresponding coefficients, which are categorized as 'morphological' features. In addition, RR interval information is also obtained characterizing the 'rhythm' around the corresponding heartbeat providing 'dynamic' features. These two different types of features are then concatenated and Support Vector Machine (SVM) is utilized for the classification of heartbeats into 15 classes. The procedure is applied to the data from two ECG leads independently and the two results are fused for the final decision. Compare the two classification results and the classification result is kept if the two are identical or the one with greater classification confidence is picked up if the two are inconsistent. The proposed method was tested over the entire MIT-BIH Arrhythmias Database [1] and it yields an overall accuracy of 99.66% on 85945 heartbeats, better than any other published results.
authors.affiliation: Department of Electrical & Computer Engineering, Carnegie Mellon University, USA.
authors.forename: Can
authors.initials: C
authors.lastname: Ye
authors.forename: Miguel Tavares
authors.initials: MT
authors.lastname: Coimbra
authors.forename: B K
authors.initials: BK
authors.lastname: Vijaya Kumar
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D016011:Normal Distribution; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic