Abstract
A new method for premature ventricular contraction (PVC) detection and classification is presented. The proposed algorithm is constituted of two principal phases: the features extraction and reduction phase and the optimized classification phase. In the first phase, the discrete cosine transform (DCT) and the continuous wavelet transform (CWT) are applied on each ECG beat to generate an augmented features vector. For the optimized classification phase, the radial basis function (RBF) neural network classifier is trained and optimized by the bat algorithm. For the aim of performances evaluation of the proposed method, the MIT-BIH arrhythmia database has been used. Consequently, the BAT-RBF classifier yielded an overall sensitivity of 95,2% and an accuracy of 98,2%, confirming clearly the competitiveness of the proposed method compared to some recent and powerful algorithms.
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