Classification of ECG arrhythmia by a modular neural network based on Mixture of Experts and Negatively Correlated Learning

In this paper, we propose a novel ECG arrhythmia classification method using the complementary features of Mixture of Experts (ME) and Negatively Correlated Learning (NCL). Negative Correlation Learning and Mixture of Experts methods utilize different error functions for simultaneous training of negatively correlated Neural Networks. The capability of a control parameter for NCL is incorporated in the error function of ME, which enables the training algorithm of ME to establish a balance in bias-variance-covariance trade-offs. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed classification method not only preserves the advantages and alleviates the disadvantages of its basis approaches, but also offering significantly improved performance over the original methods.

► Mixture of Experts (ME).
► Negatively Correlated Learning (NCL).
► Neural network ensembles.
► Classifier fusion.
► Arrhythmia classification.
► Stationary wavelet transform (SWT).