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首页> 外文期刊>Knowledge-Based Systems >Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform
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Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

机译：使用LDA，PCA，ICA和离散小波变换自动诊断冠心病患者

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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 (DWT). 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.
机译：冠状动脉疾病（CAD）是向心脏供应血液和氧气的血管变窄。心电图（ECG）是重要的心脏信号，代表数百万个心脏细胞去极化电位的总和。它包含对患心脏疾病的健康状况和性质的重要见解。但是，很难感知到表示特定类型的心脏异常的ECG信号的细微变化。因此，我们将心电图的心率信号用于心脏健康的诊断。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状况的方法。使用离散小波变换（DWT）将心率信号分解为子频带。为了减少数据量，将主成分分析（PCA），线性判别分析（LDA）和独立成分分析（ICA）应用于从特定子带提取的DWT系数集。将选定的特征集输入到四个不同的分类器中：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）。我们的结果表明，与其他数据缩减技术（PCA和LDA）和分类器相比，ICA结合GMM分类器组合可实现96.8％的最高准确性，100％的灵敏度和93.7％的特异性。总体而言，与以前的技术相比，我们提出的策略更适合于以更高的准确性诊断CAD。

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