Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals
Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals
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DOI：
10.1109/smc.2016.7844296
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发表时间：
2016-10
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影响因子：
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通讯作者：
C. Sridhar;U. Acharya;Senior Member;H. Fujita;Muralidhar G. Bairy
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作者：
C. Sridhar;U. Acharya;Senior Member;H. Fujita;Muralidhar G. Bairy
冠状动脉疾病（CAD）是导致心绞痛、心肌梗死（MI）和心源性猝死（SCD）的危险心脏病之一。冠心病是一种心脏疾病，在动脉内壁形成鼠疫，导致到达心脏肌肉的血液阻塞。心电图（ECG）是心脏信号，它代表心脏在胸部表面调节的去极化和复极化。心电图振幅和持续时间的微小变化说明了不同的病理状况，这在视觉上是乏味的。因此，计算机辅助诊断系统被用于监测心电信号。在本工作中，CAD的自动诊断使用离散小波变换（DWT）和非线性特征提取技术，如；多元多尺度熵（MMSE）、Tsallis熵和renyi熵。DWT后提取的特征根据t值进行排序，并提供给K近邻（KNN）、支持向量机（SVM）、概率神经网络（PNN）和决策树（DT）分类器，用于正常和CAD类的自动分类。使用KNN分类器，该方法的准确率最高，达到98.67%。因此，该系统可以帮助临床医生更快、更准确地诊断CAD，从而为适当的治疗提供充足的时间。
Coronary Artery Disease (CAD) is one of the hazardous heart disease which results in angina, Myocardial Infarction (MI) and Sudden Cardiac Death (SCD). CAD is a cardiac disorder in which a plague develops in the interior wall of the arteries resulting in blockage of blood reaching to the heart muscles. Electrocardiogram (ECG) is the cardiac signal which represents cardiac depolarisation and repolarisation regulated at the surface of the chest. The minute variations in amplitude and duration in the ECG wave specifies different pathological conditions which are tedious to interpret visually. Hence computer aided diagnostic systems are used to monitor ECG signals. In the present work, automated diagnosis of CAD is done using Discrete Wavelet Transform (DWT) and nonlinear feature extraction techniques like; Multivariate Multi-scale Entropy (MMSE), Tsallis entropy and renyi entropies. The extracted features after DWT are ranked based on t-value and fed to K Nearest Neighbour (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Decision Tree (DT) classifiers for automated classification of normal and CAD classes. This technique provided the highest accuracy of 98.67% using KNN classifier. Hence, the proposed system can aid clinicians in faster and accurate diagnosis of CAD and thereby provide sufficient time for proper treatment.