Classification of cardiac arrhythmias based on dual tree complex wavelet transform

The electrocardiogram (ECG) is a standard diagnostic tool to distinguish the different types of arrhythmias. This paper develops a novel framework for feature extraction technique based on dual tree complex wavelet transform (DTCWT). The feature set comprises of complex wavelet coefficients extracted from the 4th and 5th scale of DTCWT decomposition and four other features (AC power, kurtosis, skewness and timing information). This feature set is classified using feed forward neural network. In this work, five types of ECG beats (Normal, Paced, Right Bundle Branch Block, Left Bundle Branch Block and Premature Ventricular Contraction) are classified from the MIT-BIH arrhythmia database. The performance of the proposed method is compared with statistical features extracted using discrete wavelet transform (DWT). The experimental result shows that the proposed method classifies ECG beats with an overall sensitivity of 97.80%.

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