Arrhythmia detection based on morphological and time-frequency features of T-wave in electrocardiogram

As the T-wave section in electrocardiogram (ECG) illustrates the repolarization phase of heart activity, the information which is accumulated in this section is so significant that it can explain the proper operation of electrical activities in heart. Long QT syndrome (LQT) and T-Wave Alternans (TWA) have imperceptible effects on time and amplitude of T-wave interval. Therefore, T-wave shapes of these diseases are similar to normal beats. Consequently, several T-wave features can be used to classify LQT and TWA diseases from normal ECGs. Totally, 22 features including 17 morphological and 5 wavelet features have been extracted from T-wave to show the ability of this section to recognize the normal and abnormal records. This recognition can be implemented by pre-processing, T-wave feature extraction and artificial neural network (ANN) classifier using Multi Layer Perceptron (MLP). The ECG signals obtained from 142 patients (40 normal, 47 LQT and 55 TWA) are processed and classified from MIT-BIH database. The specificity factor for normal, LQT, and TWA classifications are 99.89%, 99.90%, and 99.43%, respectively. T-wave features are one of the most important descriptors for LQT syndrome, Normal and TWA of ECG classification. The morphological features of T-wave have also more effect on the classification performance in LQT, TWA and normal samples compared with the wavelet features.