Performance Analysis of Nonlinear Features in Detection of Myocardial Infarction Patients

Year 2024, Volume: 24 Issue: 6, 1499 - 1505, 02.12.2024

Ali Narin , Merve Keser

https://doi.org/10.35414/akufemubid.1427677

Abstract

Myocardial infarction (MI), one of the heart diseases, is a condition in which the heart muscle is damaged as a result of partial or complete interruption of blood flow to the regions of the heart. This condition causes permanent damage to the heart and poses a life-threatening risk. Electrocardiogram (ECG) signals, which can be obtained easily and cheaply, are used by experts for the detection of MI. However, MI-related abnormalities on some ECG signals may be overlooked or even interpreted differently. Work continues on automatic MI detection with artificial intelligence-based decision support systems in order to solve the problems encountered. In this study, lead-II derivation from 12-lead ECG signals of 52 normal and 148 MI individuals was analyzed. By using the features obtained by five different methods, namely Shannon entropy, Renyi entropy, Wavelet entropy, Kolmogorov-Sinai entropy and Fuzzy entropy, the performances in detecting healthy and MI were investigated. The performances of each entropy measure on noisy and noiseless ECG signals are compared. Performances on MI detection were analyzed using k-nearest neighbor (kNN), Naive Bayes and Ensemble classifier algorithms. As a result of the classification of the features obtained from five different methods, the highest accuracy value belongs to Fuzzy entropy with 87.72%. This value is obtained as a result of using kNN classifier on noisy signals. By classifying all features together, 90.99% overall accuracy, 95.58% sensitivity and 71.55% specificity values were obtained. This highest value was obtained as a result of the use of noisy signal and Ensemble classifier.

Keywords

Myocardial Infarction, Electrocardiogram, Feature extraction, Ensemble classifier

Miyokard Enfarktüsü Hastalarının Tespitinde Doğrusal Olmayan Özniteliklerin Performans Analizi

Abstract

Kalp rahatsızlıklarından biri olan Miyokard enfarktüsü (ME), kalbin bölgelerine kısmen veya tamamen kan akışının kesilmesi sonucunda kalp kaslarına zarar vermesi durumudur. Bu durum kalbe kalıcı hasar vermekte ve hayati risk oluşturmaktadır. ME tespiti için kolay ve ucuz elde edilebilen elektrokardiyogram (EKG) sinyalleri uzmanlar tarafından kullanılmaktadır. Fakat, bazı EKG sinyalleri üzerinde ME ile ilişkili anormallikler gözden kaçırılabilmekte hatta farklı yorumlanabilmektedir. Karşılaşılan problemlere çözüm olması amacıyla yapay zekâ tabanlı karar destek sistemleri ile otomatik ME tespiti üzerinde çalışmalar devam etmektedir. Bu çalışmada 52 sağlıklı ve 148 ME bireye ait 12 derivasyonlu EKG sinyallerinden lead-II derivasyonu analiz edilmiştir. Shannon entropi, Renyi entropi, Dalgacık entropi, Kolmogorov-Sinai entropi ve Bulanık entropi olmak üzere beş farklı yöntem ile elde edilen öznitelikler kullanılarak sağlıklı ve ME tespitindeki başarımlar araştırılmıştır. Her bir entropi ölçümünün gürültülü ve gürültüsüz EKG sinyalleri üzerinde performansları karşılaştırılmıştır. K-en yakın komşu (kNN), Naive Bayes ve Topluluk sınıflandırıcı algoritmaları kullanılarak ME tespiti üzerinde performansları analiz edilmiştir. Beş farklı yöntemden elde edilen özniteliklerin sınıflandırılması sonucu en yüksek doğruluk değeri %87,72 ile Bulanık entropi kullanılarak elde edilmiştir. Bu değer, gürültülü sinyallerin üzerinde kNN sınıflandırıcısının kullanılması sonucunda elde edilmiştir. Tüm özniteliklerin birlikte kullanılarak sınıflandırılması ile %90,99 genel doğruluk, %95,58 hassasiyet, %71,55 özgünlük değerleri elde edilmiştir. En yüksek bu değer, gürültülü sinyal ve Topluluk sınıflandırıcı kullanımı sonucunda elde edilmiştir.

Keywords

Miyokard enfarktüsü, Elektrokardiyogram, Öznitelik çıkarma, Topluluk sınıflandırıcı

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