Electrocardiography is a useful diagnostic tool for various cardiovascular diseases, such as myocardial infarction (MI). An electrocardiograph (ECG) records the electrical activity of the heart, which can reflect any abnormal activity. MI recognition by visual examination of an ECG requires an expert’s interpretation and is difficult because of the short duration and small amplitude of the changes in ECG signals associated with MI. Therefore, we propose a new method for the automatic detection of MI using ECG signals. In this study, we used maximal overlap discrete wavelet transform to decompose the data, extracted the variance, inter-quartile range, Pearson correlation coefficient, Hoeffding’s D correlation coefficient and Shannon entropy of the wavelet coefficients and used the k -nearest neighbor model to detect MI. The accuracy, sensitivity and specificity of the model were 99.57%, 99.82% and 98.79%, respectively. Therefore, the system can be used in clinics to help diagnose MI.

中文翻译：

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使用从 12 导联心电图中提取的强大特征自动检测心肌梗死

心电图是各种心血管疾病的有用诊断工具，例如心肌梗塞 (MI)。心电图 (ECG) 记录心脏的电活动，可以反映任何异常活动。通过目视检查 ECG 识别 MI 需要专家的解释，并且由于与 MI 相关的 ECG 信号的变化持续时间短且幅度小，因此很困难。因此，我们提出了一种使用 ECG 信号自动检测 MI 的新方法。本研究采用最大重叠离散小波变换对数据进行分解，提取小波系数的方差、四分位距、Pearson相关系数、Hoeffding's D相关系数和Shannon熵，并使用k-最近邻模型进行检测米。准确性，该模型的敏感性和特异性分别为99.57%、99.82%和98.79%。因此，该系统可用于临床以帮助诊断 MI。