Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals

• Type: Article in International Scientific Journal
• Date: 2012

Authors

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

Journal

Title: IEEE Transactions on Biomedical Engineering

Vol. 59

Pages: 2930-2941

ISSN: 0018-9294

Publisher: IEEE

Indexing

ISI Web of Knowledge - 327 Citations

Scopus - 406 Citations

Scientific classification

FOS: Engineering and technology > Environmental biotechnology

Other information

Authenticus ID: P-002-54A

DOI: 10.1109/tbme.2012.2213253

Abstract (EN): In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MITBIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the "class-oriented" evaluation and an accuracy of 86.4% in the "subject-oriented" evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12