Classification of Electrocardiogram Signals using Principal Component Analysis and Levenberg Marquardt Backpropagation for Detection Ventricular Tachyarrhythmia

Abstract Ventricular Tachyarrhythmia (VT) are the primary arrhythmias which are cause of sudden death. For someone who already has symptoms of VT should immediately perform an examination of one of them by using an electrocardiogram (ECG). An electrocardiogram is a recording of the heart's electrical results in a waveform. However, limited ability in analysis and diagnosis of ECG reading is still difficult to do. Therefore, the classification of ECG signals is needed to detect a person, especially those with VT or not. In this research focuses on the classification of VT heartbeats from ECG signals by using median filter method in preprocessing, Principal Component Analysis (PCA) as feature extraction and modified Backpropagation (MBP) as classification. This research used machine learning method that is a neural network with backpropagation modification that is Levenberg Marquardt to speed up network training process. The best VT detection performance results were based on the average accuracy of the overall scheme of 91.67% with the best parameters that principal component=10 and 20, hidden neuron=4, and µ value=0.001 as well training time 1 seconds with a comparison of train data and test data that is 80:20 percent. Keywords: Electrocardiogram, Levenberg Marquardt Backpropagation, Median filter, Principal Component Analysis, and Ventricular Tachyarrhythmia