Time-frequency approach to ECG classification of myocardial infarction


KAYIKÇIOĞLU İ., AKDENİZ F., KÖSE C., KAYIKÇIOĞLU T.

COMPUTERS & ELECTRICAL ENGINEERING, vol.84, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 84
  • Publication Date: 2020
  • Doi Number: 10.1016/j.compeleceng.2020.106621
  • Title of Journal : COMPUTERS & ELECTRICAL ENGINEERING

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. (c) 2020 Elsevier Ltd. All rights reserved.