<![CDATA[ECG signals analysis at first associated to pattern recognition of the ECG signals marphology. Nonetheless the signals marphology varying not only in different patients but also in the same patient. The varying of the ECG marphology has efected difficulties in ECG analysis, particularly for a trainingless medicines. On the other hand the ECG signals contain much noises. Therefore it was require the suitable methods for ECG signals analysis. This research aim are analyzing and classifying of the ECG signals from heart condition of normal, arrhytmia, ventricular tachyarrhytmia, intracardiac atrial fibrillation dan myocard infarction based on wavelet transformation and artificial neural network backpropagation.The research stages are data preparing, pre-processing, feature extraction, processing and post-processing. The 60/50 Hz noises in ECG signals from power line interference reduced using IIR notch filter with pole-zero placement method. The baseline wander noises reduced using discrete wavelet transform of 11 level decomposition to find frequency component below 0,5 Hz as a noise source.Based on this work results obtained that average accuracy percentage of the neural network recognized all of the ECG types reached 87,424 %. Highest accuracy percentage of 95,455 % for ventricular tachyarrhytmia and lowest accuracy percentage of 70 % for arrhytmia classification.]]>
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