Heartbeat Time Series Classification With Support Vector Machines

• Published in: IEEE transactions on information technology in biomedicine Vol. 13; no. 4; pp. 512 - 518
• Main Authors: Kampouraki, A., Manis, G., Nikou, C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2009
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Artificial Intelligence; Coronary Artery Disease - physiopathology; Electrocardiography; Feature extraction; Heart beat; Heart Rate - physiology; heart rate variability (HRV); heartbeat time series; Humans; Male; Models, Statistical; Neural networks; Senior citizens; Signal analysis; Signal Processing, Computer-Assisted; Signal to noise ratio; Statistical analysis; support vector machine (SVM); Support vector machine classification; Support vector machines
• ISSN: 1089-7771; 1558-0032; 1558-0032
• DOI: 10.1109/TITB.2008.2003323

In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease.