Ischemia detection via dynamic time warping and fuzzy rules

Cardiac ischemia is one of the major causes of mortalities in the world. This disease includes a wide range of temporarily disorders such as insufficient blood circulation to myocardium, which leads to myocardial infarction, and consequently, sudden death. Recording electrocardiogram (ECG) signal has an important role in diagnosis of cardiac disease due to its advantages such as easy recording and suitable cost. Generally using techniques based on extraction of ST segments from ECG signal have been always of interest for ischemia detection. In this study, dynamic time warping (DTW) method is employed as a full-automatic tool in diagnosis of ischemic areas. Then, a fuzzy classifier is applied on the extracted features from the ST segment in order to separate the healthy and ischemic cases. The simulation results show an accuracy of 93% in the output of proposed ischemia detection algorithm.