Fan Cheng-Hsiang, Hsu Yu, Yu Sung-Nien, Lin Jou-Wei
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:7334-7. doi: 10.1109/EMBC.2013.6611252.
In this study, we propose to use morphological features that are easy to identify to differentiate myocardial ischemic beats from normal beats. In general, myocardial ischemia causes alterations in electrocardiographic (ECG) signal such as deviation in the ST segment. When the ST segment level deviates from a certain voltage, the beat would be diagnosing as myocardial ischemia. To emphasize on ST variations, the QRS complex of the ECG signal was first subtracted and replaced with a straight line. Five-level discrete wavelet transform (DWT) followed to decompose the waveform into subband components and the A5 subband, which is most sensitive to the changes in the ST segment, was reconstructed for the calculation of 12 morphological features. The support vector machine (SVM) and the 10-fold cross-validation method were employed to evaluate the performance of the method. The results show high values of 95.20%, 93.29%, and, 93.63% in sensitivity, specificity, and accuracy, respectively, that were demonstrated to outperform the other methods in the literature.
在本研究中，我们建议使用易于识别的形态学特征来区分心肌缺血性搏动与正常搏动。一般来说，心肌缺血会导致心电图（ECG）信号发生改变，如ST段偏移。当ST段水平偏离某一电压时，该搏动将被诊断为心肌缺血。为了突出ST段变化，首先对ECG信号的QRS波群进行减法运算并用一条直线代替。随后进行五级离散小波变换（DWT），将波形分解为子带分量，并重建对ST段变化最敏感的A5子带，以计算12个形态学特征。采用支持向量机（SVM）和十折交叉验证法来评估该方法的性能。结果显示，灵敏度、特异性和准确率分别高达95.20%、93.29%和93.63%，证明优于文献中的其他方法。
