Computer Aided Diagnosis of Cardiac Arrhythmias

2006 International Conference on Computer Engineering and Systems Pub Date : 2006-11-01 DOI:10.1109/ICCES.2006.320458

Marwa M. A. Hadhoud, M. Eladawy, A. Farag

引用次数: 27

Abstract

The early detection of arrhythmia is very important for the cardiac patients. This is done by analyzing the electrocardiogram (ECG) signals and extracting some features from them. These features can be used in the classification of different types of arrhythmias. In this paper, we present three different algorithms of features extraction: Fourier transform (FFT), autoregressive modeling (AR), and principal component analysis (PCA). The used classifier is artificial neural networks (ANN). We observed that the system that depends on the PCA features give the highest accuracy. The proposed techniques deal with the whole 3 second intervals of the training and testing data. We reached the accuracy of 92.7083% compared to 84.4% for the reference that work on a similar data

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心律失常的计算机辅助诊断

心律失常的早期发现对心脏病患者非常重要。这是通过分析心电图信号并从中提取一些特征来实现的。这些特征可用于不同类型心律失常的分类。在本文中，我们提出了三种不同的特征提取算法:傅里叶变换(FFT)，自回归建模(AR)和主成分分析(PCA)。使用的分类器是人工神经网络(ANN)。我们观察到，依赖于PCA特征的系统给出了最高的准确性。所提出的技术处理训练和测试数据的整个3秒间隔。我们达到了92.7083%的准确率，而在类似数据上工作的参考文献的准确率为84.4%

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2006 International Conference on Computer Engineering and Systems

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