NDLI: Arrhythmias Classification with MLP Neural Network and Statistical Analysis

Content Provider	IEEE Xplore Digital Library
Author	Raut, R.D. Dudul, S.V.
Copyright Year	2008
Description	Author affiliation: P. G. Dept. Appl. Electron., Amravati Univ., Amravati (Raut, R.D.; Dudul, S.V.)
Abstract	This paper presents a classification system for cardiac arrhythmias using artificial neural network (ANN) with back propagation algorithm. Classifiers based on multi layer perceptron (MLP) and discriminant analysis study using XLSTAT statistical classifier software are thoroughly examined on the UCI machine learning data base for cardiac arrhythmias. For this multi class classification we used one against rest method to classify 16 different arrhythmias which include normal sinus rhythm, Ischemic changes, myo infarction, sinus bradycardia, sinus tachycardia, premature ventricular contraction, supraventricular premature contraction, bundle branch block, atrial fibrillation, atrial flutter, left ventricular hypertrophy and atrioventricular block. From exhaustive and careful experimentation, we reached to the conclusion that proposed MLPNN classifier ensures true estimation of the complex decision boundaries, remarkable discriminating ability and does outperform the statistical discriminant analysis and classification tree rule based prediction.
Starting Page	553
Ending Page	558
File Size	130896
Page Count	6
File Format	PDF
ISBN	9780769532677
DOI	10.1109/ICETET.2008.260
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Accuracy Databases Artificial neural networks Classification algorithms MLP Neural Network Classification tree analysis
Content Type	Text
Resource Type	Article

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