Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform
Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform
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DOI：
10.1016/j.knosys.2012.08.011
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发表时间：
2013-01-01
影响因子：
8.8
通讯作者：
Suri, Jasjit S.
中科院分区：
文献类型：
--
作者：
Giri, Donna;Acharya, U. Rajendra;Suri, Jasjit S.
冠状动脉疾病（CAD）是指为心脏提供血液和氧气的血管变窄。心电图（ECG）是一个重要的心脏信号，代表了心脏细胞去极化电位的总和。它包含了对健康状况和折磨心脏的疾病性质的重要见解。然而，很难察觉ECG信号中指示特定类型的心脏异常的细微变化。因此，我们使用来自ECG的心率信号来诊断心脏健康。在这项工作中，我们提出了一种使用心率信号自动检测正常和冠状动脉疾病的方法。心率信号使用离散小波变换（自己的）分解成频率子带。主成分分析（PCA），线性判别分析（LDA）和独立成分分析（伊卡）被应用于从特定子带提取的DWT系数集，以减少数据维数。选择的特征集被送入四个不同的分类器：支持向量机（SVM），高斯混合模型（GMM），概率神经网络（PNN）和K-最近邻（KNN）。我们的研究结果表明，伊卡耦合GMM分类器组合导致最高的准确性为96.8%，灵敏度为100%，特异性为93.7%，相比其他数据减少技术（PCA和LDA）和分类器。总体而言，与以前的技术相比，我们提出的策略更适合诊断CAD具有更高的准确性。(C)2012爱思唯尔有限公司版权所有。
Coronary Artery Disease (CAD) is the narrowing of the blood vessels that supply blood and oxygen to the heart. Electrocardiogram (ECG) is an important cardiac signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insights into the state of health and nature of the disease afflicting the heart. However, it is very difficult to perceive the subtle changes in ECG signals which indicate a particular type of cardiac abnormality. Hence, we have used the heart rate signals from the ECG for the diagnosis of cardiac health. In this work, we propose a methodology for the automatic detection of normal and Coronary Artery Disease conditions using heart rate signals. The heart rate signals are decomposed into frequency sub-bands using Discrete Wavelet Transform (own. Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were applied on the set of DWT coefficients extracted from particular sub-bands in order to reduce the data dimension. The selected sets of features were fed into four different classifiers: Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN) and K-Nearest Neighbor (KNN). Our results showed that the ICA coupled with GMM classifier combination resulted in highest accuracy of 96.8%, sensitivity of 100% and specificity of 93.7% compared to other data reduction techniques (PCA and LDA) and classifiers. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of CAD with higher accuracy. (C) 2012 Elsevier B.V. All rights reserved.