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

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 informat...