Optimization of a Multi-class MLP ECG Classifier Using FCM

ECG pattern classification using MLP is an effective and robust technique. Due to the inherent structure of MLP and training algorithm, MLPs tend to be slow and bulky in terms of the hidden layer neurons. This condition is aggravated further if the input data dimension is large as in the case of ECG. The paper addresses this problem by optimizing the MLP using an additional clustering of the feature extracted data. Considerable reduction in the size of MLP was recorded (max. 67.9%) with a reduction in training time (maximum of 59.81%). The experimentation used benchmark arrhythmia database from Physionet Massachusetts institute of technology-Beth Israel hospital (MIT-BIH). Four feature extraction methodologies were subjected to fuzzy c-means clustering for obtaining optimized MLPs. Ten statistical morphological features were also considered for designing the MLP classifiers.