MLP神经网络的心律失常分类及统计分析

R. Raut, Dr. Sanjay Vasant Dudul
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引用次数: 31

摘要

提出了一种基于反向传播算法的人工神经网络(ANN)心律失常分类系统。在UCI心律失常机器学习数据库上,对基于多层感知器(MLP)的分类器和基于XLSTAT统计分类器软件的判别分析研究进行了深入的研究。对于这种多类别的分类,我们使用一对休息方法对16种不同的心律失常进行分类,包括正常窦性心律、缺血性改变、肌梗死、窦性心动过缓、窦性心动过速、室性早搏、室上早搏、束支传导阻滞、心房颤动、心房扑动、左室肥厚和房室传导阻滞。经过详尽细致的实验,我们得出结论,所提出的MLPNN分类器能够保证对复杂决策边界的真实估计,具有显著的判别能力,并且优于基于统计判别分析和分类树规则的预测。
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Arrhythmias Classification with MLP Neural Network and Statistical Analysis
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.
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