ECG Signal Analysis for the Detection of Myocardial Infarction

A Myocardial infarction (MI), commonly known as a heart attack, occurs when blood flow decreases or stops in one of the coronary arteries of the heart, causing damage to the heart muscle. This lack of blood flow can occur because of many different factors but is usually related to a blockage in one or more coronary arteries. Without blood flow, the affected heart muscle will begin to die. If the blood flow is not restored quickly, a heart attack can occur and cause permanent heart damage and may lead to death. In this work, a feature extraction method for the detection of MI is proposed. The proposed method helps to diagnose the MI at an early stage. The method involves feature extraction from the ECG segments of normal subjects and patients suffering from MI. The algorithms are developed to estimate the power spectrum of the ECG signal segments. The power spectrum is estimated using Welch method and features are extracted. The cluster analysis of the extracted features is done. The results show that the extracted features are showing good separation and can be used for the detection of MI.