Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
This study presents a multi-stage system for reliable heart rhythm monitoring and diagnosis. It is comprised of three components including data pre-processing and feature extraction, abnormal arrhythmia detection and diagnosis. In the first stage, three different feature extraction methods are applied together to obtain a composite representation of the ECG waveform. In the second stage, the Multivariate Statistical Process Monitoring (MSPM) approach is used to capture the natural variations of the normal cardiac state and to detect any abnormal arrhythmia. Then a feed-forward neural network is used to classify the abnormal arrhythmia in 5 different classes. The results of experiments show the good performance of the proposed system.
How to cite this article:
Nadia Bouteraa , Salah Chenikher , Noureddine Doghmane and Messaoud Ramdani . Cardiac Arrhythmia Detection and Classification Based on Subspace Approach and Neural Networks.
DOI: https://doi.org/10.36478/ajit.2007.1167.1173
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2007.1167.1173