Arrhythmia Classification by Local Fractional Fourier Transform

Abstract

Automated analysis of electrocardiography (ECG) signals compose a system for early detection of heart disorders. One of the most important parts of ECG signal classification system is to produce the discriminative features for proper identification of heart disorders. Fractional Fourier Transform (FrFT) as the generalized form of Fourier Transform (FT) gives a hybrid time-frequency representation based on an angle parameter. A genuine method called Local Fractional Fourier Transform (LFrFT) is proposed by means of exploiting local features for non-stationary signals such as heart beats. Experimental results are given for LFrFT features extracted from MIT-BIH arrhythmia ECG dataset with different angle parameters on several classifiers.