Detection of premature ventricular contractions using MLP neural networks: A comparative study

• Published in: Measurement : journal of the International Measurement Confederation Vol. 43; no. 1; pp. 103 - 112
• Main Authors: Ebrahimzadeh, Ataollah, Khazaee, Ali
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2010
• Subjects: ECG beat classification; MLP neural network; Premature ventricular contraction; Training algorithms; Wavelet transform; ECG beat classification; Premature ventricular contraction; Wavelet transform; Training algorithms; MLP neural network
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2009.07.002

This paper proposes a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising module, a feature extraction module and a classification module. In the first module we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. The feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptron (MLP) neural networks with different number of layers and nine training algorithms are designed. The performances of the networks for speed of convergence and accuracy classifications are evaluated for seven files from the MIT–BIH arrhythmia database. Among the different training algorithms, the resilient back-propagation (RP) algorithm illustrated the best convergence rate and the Levenberg–Marquardt (LM) algorithm achieved the best overall detection accuracy.