Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 59, Issue 10, Pages 2930-2941Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2012.2213253
Keywords
Heartbeat classification; independent component analysis; support vector machine; wavelet transform
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Funding
- Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) [SFRH/BD/33519/2008, CMU-PT/CPS/0046/2008]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/33519/2008, CMU-PT/CPS/0046/2008] Funding Source: FCT
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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.
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