dc.contributor.author | Kayikcioglu, İ. | |
dc.contributor.author | Akdeniz, F. | |
dc.contributor.author | Köse, C. | |
dc.contributor.author | Kayikcioglu, T. | |
dc.date.accessioned | 2020-12-19T20:18:09Z | |
dc.date.available | 2020-12-19T20:18:09Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 0045-7906 | |
dc.identifier.uri | https://doi.org/10.1016/j.compeleceng.2020.106621 | |
dc.identifier.uri | https://hdl.handle.net/11436/4489 | |
dc.description.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. © 2020 Elsevier Ltd | en_US |
dc.description.sponsorship | 114E452 Türkiye Bilimsel ve Teknolojik Araştirma Kurumu 114E452 Türkiye Bilimsel ve Teknolojik Araştirma Kurumu | en_US |
dc.description.sponsorship | We declare that the authors of the publication have research support from The Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant 114E452. | en_US |
dc.description.sponsorship | This research is supported by The Scientific and Technological Research Council of Turkey ( TÜBİTAK ) under Grant 114E452 . Ethics committee approval was not required because no data was collected from human subjects and all data samples were collected the three databases available at internet sites. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Classification | en_US |
dc.subject | Electrocardiogram | en_US |
dc.subject | Myocardial infarction | en_US |
dc.subject | ST segment | en_US |
dc.subject | Telemedicine | en_US |
dc.subject | Time-frequency distributions | en_US |
dc.title | Time-frequency approach to ECG classification of myocardial infarction | en_US |
dc.type | article | en_US |
dc.contributor.department | RTEÜ | en_US |
dc.identifier.doi | 10.1016/j.compeleceng.2020.106621 | |
dc.identifier.volume | 84 | en_US |
dc.relation.journal | Computers and Electrical Engineering | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |