Time-frequency approach to ECG classification of myocardial infarction

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

COMPUTERS & ELECTRICAL ENGINEERING, cilt.84, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 84
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.compeleceng.2020.106621
  • Dergi Adı: COMPUTERS & ELECTRICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Myocardial infarction, Electrocardiogram, ST segment, Classification, Time-frequency distributions, Telemedicine, ISCHEMIA DETECTION, ST SEGMENT, SIGNALS, ELECTROCARDIOGRAM, ENTROPY
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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.