<![CDATA[To diagnose heart disease, especially arrhythmia, the procedure to classify heartbeat pattern is important to do. The pattern can be find by analyze the patient Electrocardiogram (ECG) record. The change of the pattern can be the sign for more serious disease. For today, there are many research conducted to explore the method for classify the beat, but the problem still found to determine the best features set to identify and classify heartbeat pattern. In this research, a feature extraction method, based from wavelet transformation using Haar coefficients was proposed, from segmented ECG record, which represented one beat cycle. Feature was built from each decomposition's coefficients of ECG segment, with simple statistical approach, mean, standard deviation, kurtosis and skewness. MIT-BIH was used as the dataset for this research. Feature evaluation and selection are conducted using Weka software. With using Random Forest classifier, the combination of mean, standard deviation and skewness from each wavelet coefficient, are the best features, which gave the result 84% for Normal class, 98% for Premature Ventricular Contraction (PVC) and 86 % for Atrial Premature Contraction (APC).]]>
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