Detection of Abnormal Electrocardiogram (ECG) Using Wavelet Decomposition and Support Vector Machine (SVM)

Pratik Deb, Mohammad Nooruddin, Md. Shajahan Badshah
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引用次数: 1

Abstract

Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart which is obtained by placing various electrodes on some specific positions of the body surface of the subject. Abnormalities in the ECG signal of a patient may indicate cardiac diseases that need to be attended by physicians on an urgent basis. Hence, it is necessary to detect an abnormal ECG for the betterment of the patient. Such a method to classify ECG signals whether they are normal or abnormal is developed in this work. Angina, Bundle Branch Block, Cardiomyopathy Heart Failure, Dysrhythmia, Myocardial Hypertrophy, Myocardial Infarction, Myocarditis, Valvular Heart Disease: all these cardiac conditions have been classified as abnormal ECG signal in our work. First, statistical features like skewness, kurtosis, standard deviation of detail and approximation coefficients of the Daubechies wavelet (db10) of order 5 for a number of abnormal and normal ECG signals obtained in the feature extraction stage. Secondly, Support Vector Machine (SVM) was used for classification which was trained by the features extracted in the first stage. Finally, the accuracy, sensitivity, specificity of this method was checked by testing the SVM with 36 signals obtained from MIT-BIH Normal Sinus Rhythm Database and 36 signals from PTB Diagnostic ECG Database which yielded an accuracy, sensitivity, specificity 98.61%, 97.37%, 97.22% respectively.
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基于小波分解和支持向量机的异常心电图检测
心电图(ECG)是心脏电活动的图形表示,它是通过在受试者体表的某些特定位置放置各种电极而获得的。患者的心电图信号异常可能提示心脏疾病,需要医生紧急诊治。因此,有必要检测异常心电图,以改善患者的病情。本文提出了一种对心电信号进行正常或异常分类的方法。心绞痛、束支传导阻滞、心肌病心衰、心律失常、心肌肥厚、心肌梗死、心肌炎、瓣膜性心脏病:这些心脏疾病在我们的工作中都被归类为异常心电信号。首先,对特征提取阶段得到的若干异常和正常心电信号的5阶Daubechies小波(db10)的偏度、峰度、细节标准差和近似系数等统计特征进行分析。其次,利用第一阶段提取的特征进行训练的支持向量机进行分类;最后,通过对来自MIT-BIH正常窦性心律数据库的36个信号和来自PTB诊断心电数据库的36个信号进行SVM检测,分别获得了98.61%、97.37%、97.22%的准确率、灵敏度、特异性。
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