Arrhythmia Detection and Classification using Morphological and Dynamic Features of ECG Signals

• Type: Article in International Conference Proceedings Book
• Date: 2010

Authors

Can Ye (Author) Other; Miguel Tavares Coimbra (Author) FCUP; Vijaya V K V Kumar (Author) Other

Conference proceedings International

Title: 2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Pages: 1918-1921

32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

Buenos Aires, ARGENTINA, AUG 30-SEP 04, 2010

Indexing

ISI Web of Knowledge - 33 Citations

Scopus - 121 Citations

COMPENDEX

Scientific classification

FOS: Engineering and technology > Environmental biotechnology

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-005-6PE

DOI: 10.1109/iembs.2010.5627645

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

Notes: ISBN-13: 9781424441235. - DOI: 10.1109/IEMBS.2010.5627645. - Main Heading: Feature extraction; Controlled terms: Electrocardiography, Electrochromic devices, Independent component analysis, Wavelet transforms; Uncontrolled terms: Arrhythmia classification, Arrhythmia detection, Cardiac arrhythmia, Cardiac disorders, assification confidence, Classification results, Computer assisted, Dynamic features, ECG signals, Final decision, New approaches, RR intervals, Two classification.

No. of pages: 4