Author : Divya K 1
Date of Publication :7th May 2016
Abstract: Automatic detection and classification of ECG heart beats is of high value in diagnosis and treatment of patients with lifethreatening cardiac arrhythmia. Arrhythmia is an abnormal or irregular heart rhythm which reflects the bad condition of heart. Abnormal heartbeat comprises Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC), and Ventricular Premature Contraction (VPC). ECG signal analysis includes QRS complex detection, P and T peak detection, feature selection and beat classification. In this paper, time interval features of ECG signal are extracted and Gaussian Mixture Model (GMM) is used to classify the ECG signals. The ECG signals used for the evaluation are taken from MIT-BIH arrhythmia database. The Experimental results obtained after classification are 84.99%, 96.6%, 93.08%, 94.48% and 62.83% for NORM, LBBB, RBBB, VPC and APC, respectively
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