Title: Modified fuzzy ARTMAP for cardiac arrhythmia recognition
Authors: Mohammed Hakim Bendiabdallah; Mohamed Amine Chikh; Belabbas Yagoubi
Addresses: Computer Laboratory of Oran, University of Oran 1 Ahmed Ben Bella, Oran, Algeria ' Biomedical Engineering Laboratory, University of Tlemcen, Chetouane, Algeria ' Computer Laboratory of Oran, University of Oran 1 Ahmed Ben Bella, Oran, Algeria
Abstract: Cardiovascular diseases are one of the most common causes of mortality in the world. Therefore, early detection and treatment of arrhythmia remains a major challenge in cardiac care. Cardiac activity is one of the most significant tools to determine the status of the patient, which is primarily reflected by a physiological signal also known as Electrocardiogram (ECG). A number of algorithms have been proposed for the automatic recognition of cardiac arrhythmia. We propose in this paper a modified architecture of the fuzzy adaptive resonance theory - supervised predictive mapping neural network (fuzzy ARTMAP) algorithm for the diagnosis of cardiac rhythm; we introduced a statistical and a probabilistic approach in the neurons of the hidden layer, requiring only one epoch for learning. This showed a significant improvement in the obtained results, which were validated using ECG signals of different patients from the 'MIT-BIH arrhythmia databases'. Our results therefore demonstrate the effectiveness of this modified classifier compared to those already reported in the literature.
Keywords: fuzzy ARTMAP; ECG beat classification; premature ventricular contraction; MIT-BIH; biomedical signals; ECG signals; electrocardiograms; cardiac arrhythmia; arrhythmia recognition; heart rate variations; cardiovascular disease; adaptive resonance theory; neural networks; heartbeats.
International Journal of Biomedical Engineering and Technology, 2016 Vol.22 No.1, pp.79 - 97
Received: 22 Jul 2015
Accepted: 22 Dec 2015
Published online: 08 Sep 2016 *