AUTOMATIC ARRHYTHMIA DETECTION ALGORITHM USING STATISTICAL AND AUTOREGRESSIVE MODEL FEATURES
Human heart healthiness is one of the major components to determine a person’s overall healthiness. Automatic arrhythmia detection is important in a remote area where there is a lack of experienced cardiologists. In this work, an automatic arrhythmia detection algorithm is developed using statistical and autoregressive features to assist medical officers in the diagnosis of arrhythmia diseases. Basic statistical components namely mean, energy, standard deviation, mean absolute deviation, fractal, inter-quartile range and min/max value, were calculated. Alongside with statistical features, 10th order auto-regressive model parameters are used as input features to support vector machine (SVM). All features are calculated using an electrocardiogram (ECG) signals windowed into per beat manner. The proposed algorithm is able to classify normal ECG beat and five types of abnormal ECG beat; paced beat, right & left bundle branch block beat, premature ventricular contraction beat and aberrated atrial premature beat. By using SVM with quadratic and cubic kernel function, the proposed algorithm achieved the best accuracy of 95.8%.
Keywords: ECG, cascade-SVM, AR model, statistical model, heart condition, computer-aided diagnosis