Myocardial infarction (MI), usually referred as heart attack, takes place when blood circulation stops to specific portion of the heart resulting permanent damage to the heart muscles. It is an important task to identify the occurrence of MI from the ECG recordings efficiently. Most of the detection procedures include advanced signal processing methods, more ECG features and composite classifiers, making the overall procedure complex. This paper aims at automated identification of MI using modified Stockwell transform (MST) based time-frequency analysis and a phase information distribution pattern method. The morphological, pathological and temporal alterations in ECG waveforms resulting from the onset of MI are noticed in the phase distribution pattern of the ECG signal. Two discriminating features, utterly reflecting these alterations, are recognized for 12 leads of the MI affected ECG signal. Prior informations regarding the pathological characteristics of the specific disease are required for the correct detection of MI using few numbers of ECG leads. Thus, in this paper 12 lead ECG signals have been considered for identification of MI. The two-class classification problem with MI class and healthy individual class is performed using the threshold based classification regulation. Both healthy control and MI affected ECG signals are collected from the PTB diagnostic ECG database. The accuracy, sensitivity and specificity are found to be 99.93%, 99.97% and 99.30% for detection of MI. The proposed method has got the superiority in terms of simplicity of features, small feature dimension and simpler classification rule ensuring faster, accurate and easier MI detection.

心肌梗塞（MI），通常称为心脏病发作，发生在心脏特定部位的血液循环停止而对心肌造成永久性损害时。有效地从ECG记录中识别出MI的发生是一项重要任务。大多数检测程序包括高级信号处理方法，更多的ECG功能和复合分类器，从而使整个程序变得复杂。本文旨在利用基于修正的斯托克韦尔变换（MST）的时频分析和相位信息分布模式方法自动识别心梗。在心电图信号的相位分布模式中，可以注意到由心律失常引起的心电图波形的形态，病理和时间变化。完全体现这些变化的两个区别特征，被MI感染的ECG信号的12根导线识别。需要使用少量ECG导线正确检测MI所需的有关特定疾病病理特征的先验信息。因此，在本文中，已考虑使用12个前导ECG信号来识别MI。使用基于阈值的分类规则执行具有MI类和健康个体类的两类分类问题。从PTB诊断ECG数据库收集健康对照和受MI影响的ECG信号。发现MI的准确性，敏感性和特异性为99.93％，99.97％和99.30％。所提方法在特征简单，特征尺寸小和分类规则简单等方面具有优势，可确保更快，准确，更容易地进行MI检测。