Analysis of Myocardial Infarction Using Discrete Wavelet Transform

Abstract

Myocardial infarction (MI), is commonly known as a heart attack, occurs when the blood supply to the portion of the heart is blocked causing some heart cells to die. This information is depicted in the elevated ST wave, increased Q wave amplitude and inverted T wave of the electrocardiogram (ECG) signal. ECG signals are prone to noise during acquisition due to electrode movement, muscle tremor, power line interference and baseline wander. Hence, it becomes difficult to decipher the information about the cardiac state from the morphological changes in the ECG signal. These signals can be analyzed using different signal processing techniques. In this work, we have used multiresolution properties of wavelet transformation because it is suitable tool for interpretation of subtle changes in the ECG signal. We have analyzed the normal and MI ECG signals. ECG signal is decomposed into various resolution levels using the discrete wavelet transform (DWT) method. The entropy in the wavelet domain is computed and the energy–entropy characteristics are compared for 2282 normal and 718 MI beats. Our proposed method is able to detect the normal and MI ECG beat with more than 95% accuracy.