IJBTT

Automatic Arrhythmia Classification Method Using Simple Statistical Features

	International Journal of Engineering Trends and Technology (IJETT)
	© 2017 by IJETT Journal
	Volume-44 Number-3
	Year of Publication : 2017
	Authors : Khudhur A. Alfarhan, MohdYusoffMashor, Abdul Rahman MohdSaad
	DOI :  10.14445/22315381/IJETT-V44P222

Citation 

Khudhur A. Alfarhan, MohdYusoffMashor, Abdul Rahman MohdSaad "Automatic Arrhythmia Classification Method Using Simple Statistical Features", International Journal of Engineering Trends and Technology (IJETT), V44(3),107-111 February 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Arrhythmia detection and classification methods are normally involve complex procedures and it is quite hard to achieve good accuracy. Current study presents an arrhythmia detection and classification method using simple statistical features extraction methods and library support vector machine (LIBSVM) classifier. The electrocardiogram (ECG) signals for three classes, namely normal sinus rhythm (NSR), premature atrial contraction (PAC) and premature ventricle contraction (PVC) were obtained from MIT arrhythmia database. The obtained ECG signals were denoised using bandpass filter. The denoised signals were segmented into 3.34 seconds. The segmented ECG signals were normalized to prepare them for features extraction stage. The R-R intervals were calculated to extract the features from them. The features extraction methods that were used in this study are the mean absolute value (MAV) of ECG segment, root mean square (RMS) of ECG segment, the median of R-R intervals, and standard deviation (SD) of R-R intervals. The extracted features were classified using LIBSVM classifier. The achieved accuracy from this method is 98.54%. The results showed that the proposed method for arrhythmia detection and classification is accurate, simple and reasonable method comparing to the other studies.

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Keywords
Electrocardiogram (ECG), Arrhythmia, LIBSVM, Statistical Features.