Classifying Five Different Arrhythmias by Analyzing the ECG Signals
Journal Title: International Journal of Computational Engineering and Management IJCEM - Year 2012, Vol 15, Issue 4
Abstract
An electrocardiogram (ECG) is a bioelectrical signal which records the heart's electrical activity versus time. It is an important diagnostic tool for assessing heart functions. The early detection of arrhythmia is very important for the cardiac patients. ECG is a test that measures the electrical activity of the heart. The signals that make the heart's muscle fibers contract come from the sinoatrial node, which is the natural pacemaker of the heart. In an ECG test, the electrical impulses made while the heart is beating are recorded and usually shown on paper, and records any problems with the heart's rhythm. This provides the conduction of the heart beat through the heart which may be affected by heart disease. This paper aims in detecting and classifying different types of arrhythmias which is done by analyzing the electrocardiogram (ECG) signals and extracting some features from them. In this paper five diffent classes are classified which are Supraventricular arrhythmias(svdb), Maligant ventricular Ectopy database (vfdb), Congestive heart failure(afdb), Post-Ictal heart rate oscillations in partial Epilepsy (szdb) and Normal sinus rhythm(nsrdb).Three different algorithms: FFT, AR and PCA are used for features extraction. The used classifier ANN. The proposed techniques deal with the whole 3 second intervals of the training and testing data. The processed signal source came from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database.
Authors and Affiliations
Anup M. Vanage, R. H. Khade and D. B. Shinde
Keywords
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