Document Detail


Classification of arrhythmia using hybrid networks.
MedLine Citation:
PMID:  20703755     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Reliable detection of arrhythmias based on digital processing of Electrocardiogram (ECG) signals is vital in providing suitable and timely treatment to a cardiac patient. Due to corruption of ECG signals with multiple frequency noise and presence of multiple arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a challenging task. This paper focuses a Fuzzy C- Mean (FCM) clustered Probabilistic Neural Network (PNN) and Multi Layered Feed Forward Network (MLFFN) for the discrimination of eight types of ECG beats. Parameters such as fourth order Auto Regressive (AR) coefficients along with Spectral Entropy (SE) are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis of Massachusetts Institute of Technology- Beth Israel Hospital (MIT-BIH) arrhythmia database shows that FCM clustered PNNs is superior in cardiac arrhythmia classification than FCM clustered MLFFN with an overall accuracy of 99.05%, 97.14%, respectively.
Authors:
Hassan H Haseena; Paul K Joseph; Abraham T Mathew
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Publication Detail:
Type:  Journal Article     Date:  2010-03-10
Journal Detail:
Title:  Journal of medical systems     Volume:  35     ISSN:  0148-5598     ISO Abbreviation:  J Med Syst     Publication Date:  2011 Dec 
Date Detail:
Created Date:  2011-12-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7806056     Medline TA:  J Med Syst     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1617-30     Citation Subset:  IM    
Affiliation:
Department of Electrical and Electronics Engineering, M.E.S. College of Engineering, Kuttippuram, 679 573, Kerala, India, haseena_hamsa@yahoo.com.
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