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Electrocardiograph (ECG) is a recorder of human heart signals with signal output on a monitor or graph paper. The ECG records the measurement of the electrical activity of the heart from the surface of the body by a set of electrodes that are installed in such a way that reflects the tapping point activity. The pattern of ECG output signals in one heartbeat produces a pattern with a peak point P, Q, R, S and T or QRS complex. ECG signal waveform results were analyzed using Learning Vector Quantization (LVQ) Artificial Neural Networks, and grouped into two classes, namely normal and abnormal heart patterns. The normal heart condition that is trained is a medically normal heart categorized as healthy as 10 data, while an abnormal heart (Heart, Coronary Heart, and Aortic Regurgutation) is 20 data. The LVQ method recognizes the input pattern based on the proximity of the two vectors, namely the vector of the input unit or neuron with the weight vector produced by each class. Online LVQ identification (using ECG) recorded from 25 direct trials resulted in 80% accuracy.
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