Abstract Electrocardiogram (ECG) analysis is one of the most important techniques to classify myocardial infarction. It is possible to diagnose that the patient may have a heart attack with ST segment elevation or depression in the ECG recordings taken before patient has a myocardial infarction. We propose a method to classify ST segment using time-frequency distribution based features from multi-lead ECG signals. In contrast to many studies in the literature, the proposed method is based on four-class classifcation method and is tested on a large dataset consisting of three different databases, namely MIT-BIH Arrhythmia database, European ST-T database and Long-Term ST database. Among the classification algorithms, the weighted k-NN algorithm achieved the best average performance with accuracy of 94.23%, sensitivity of 95.72% and specificity of 98.15% using Choi-Williams time-frequency distribution features. Meanwhile, the speed of the proposed algorithm is suitable for telemedicine systems.

中文翻译：

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心肌梗死心电图分类的时频法

摘要 心电图 (ECG) 分析是对心肌梗死进行分类的最重要技术之一。在患者发生心肌梗塞之前拍摄的心电图记录中，可以诊断出患者可能患有心脏病并伴有 ST 段抬高或压低。我们提出了一种使用多导联 ECG 信号中基于时频分布的特征对 ST 段进行分类的方法。与文献中的许多研究相比，所提出的方法基于四类分类方法，并在由三个不同数据库组成的大型数据集上进行了测试，即 MIT-BIH 心律失常数据库、欧洲 ST-T 数据库和长期 ST数据库。在分类算法中，加权k-NN算法平均性能最好，准确率为94.23%，灵敏度为95。72% 和 98.15% 的特异性使用 Choi-Williams 时频分布特征。同时，该算法的速度适用于远程医疗系统。