Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals

• Published in: IEEE transactions on biomedical engineering Vol. 59; no. 10; pp. 2930 - 2941
• Main Authors: Ye, Can, Vijaya Kumar, B. V. K., Coimbra, Miguel Tavares
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2012; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Arrhythmias, Cardiac - classification; Arrhythmias, Cardiac - physiopathology; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Databases, Factual; Electrocardiography; Electrocardiography, Ambulatory - methods; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; Exact sciences and technology; Feature extraction; Heart beat; Heart Rate - physiology; Heart rate variability; Heartbeat classification; Humans; independent component analysis; Information, signal and communications theory; Investigative techniques, diagnostic techniques (general aspects); Medical management aid. Diagnosis aid; Medical sciences; Principal Component Analysis; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Support Vector Machine; Support vector machines; Telecommunications and information theory; Training; Wavelet Analysis; wavelet transform; Heart rate; Electrodiagnosis; Automatic classification; Wavelet transformation; Independent component analysis; wavelet transform; Electrocardiography; Signal processing; Support vector machine; Signal classification; Heartbeat classification; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2012.2213253

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 MIT-BIH 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.