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The long QT syndrome (LQTs), T-wave alternans (TWA), and ventricular tachyarrhythmia (VT) are some of the common cardiac diseases which cause sudden cardiac death (SCD) in the world. [1,2] Many studies have been developed to detect an abnormal sinus ECG based on the features of ECG signal. Most of these articles use QRS complex to indentify the arrhythmia of the heart. One of the traditional methods has been performed by Jain [3] that digitized and represented each ECG lead by its z-domain modes to enhance the discrimination of the subtle changes in P, QRS, and T sections, the derivatives of the waves are employed for extraction of the modes. Lin et al. [4] used linear prediction to extract features from QRS complexes. Osowski et al. [5] applied fuzzy neural network to ECG beat recognition and classification and the features drawn from the higher order statistics have been proposed in the study. Also Engin [6] performed similar method and used autoregressive model coefficients, higher-order cumulant, and wavelet transform variances as features to enhance the performance. Jekova et al. [7] implemented four different classifiers based on 26 morphological features
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