Chashmi Abdullah Jafari, Amirani Mehdi Chehel
Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.
J Electr Bioimpedance. 2019 Aug 20;10(1):47-54. doi: 10.2478/joeb-2019-0007. eCollection 2019 Jan.
Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.
利用计算机辅助诊断（CAD）机器对心脏病进行初步识别，可降低心脏病患者的高死亡率。识别心脏异常是一项艰巨的任务，因为心电图信号的细微变化可能无法仅凭肉眼准确识别。本文提出了一种基于离散小波变换、高阶统计特征提取和基于熵的特征选择方法相结合的有效心电图心律失常诊断方法。使用神经网络和支持向量机对五类心跳类别进行分类。应用神经网络和支持向量机方法，我们提出的系统能够分别以99.83%和99.03%的高精度对心律失常类别进行分类。与其他最近提出的方法相比，所提出方法在准确性方面的优势已通过实验得到证明。
