Key result
BAT-RBF classifier using DCT and CWT features achieves ~98% accuracy for PVC detection.
A novel DCT-CWT based discriminant and optimized RBF neural network algorithm demonstrates high accuracy and sensitivity for automated PVC detection.
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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Harkat et al. (2023) studied Premature ventricular contraction (PVC). BAT-RBF classifier with DCT and CWT features vs. Recent and powerful algorithms was evaluated on Overall sensitivity and accuracy for PVC detection and classification. The BAT-RBF classifier using DCT and CWT features achieved an overall sensitivity of 95.2% and an accuracy of 98.2% for premature ventricular contraction detection on the MIT-BIH database.
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