Time-frequency approach to ECG classification of myocardial infarction
Ilknur Kayikcioglu, Cemal Kose, Fulya Akdeniz, Temel Kayikcioglu
article (Uluslararası, Hakemli) | Yazar(lar): Ilknur Kayikcioglu, Cemal Kose, Fulya Akdeniz, Temel Kayikcioglu | Yıl: 2020 | Dergi/Kurum: COMPUTERS & ELECTRICAL ENGINEERING | İndeks: WOS.SCI | DOI: 10.1016/j.compeleceng.2020.106621 | Anahtar Kelimeler: Myocardial infarction, Electrocardiogram, ST segment, Classificatio
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.