FEATURE SELECTION IN FREQUENCY DOMAIN FOR STATIONARY ECG SIGNAL FOR ECG BEAT CLASSIFICATION
Keywords:
Cardiac Arrhythmia, Discrete Cosine Transform (DCT), Electrocardiograms (ECG), RR Waves, Soft ComputingAbstract
Arrhythmia is a common term used for cardiac rhythm deviating from normal sinus rhythm. Many heart diseases are detected through electrocardiograms (ECG) analysis. Manual analysis of ECG is time consuming and error prone. Thus, an automated system for detecting arrhythmia in ECG signals is essential. In this paper, investigations are carried out to evaluate the performance of soft computing techniques for classifying cardiac arrhythmia. In this study, features are extracted from time series ECG data with Discrete Cosine Transform (DCT) computing the distance between RR waves. The feature is the beat’s extracted RR interval. Frequency domain extracted features are classified using Classification and Regression Tree (CART), Radial Basis Function (RBF), Support Vector Machine (SVM) and Multilayer Perceptron Neural Network (MLP-NN). Experiments were conducted on the MIT-BIH arrhythmia database.
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