Koroner Kalp Hastalıklarının Kinetik Özelliklere Göre Analizi: EKG Sinyallerine Varyasyonel Mod Ayrıştırmanın Uygulanması ve Makine Öğrenme Algoritmaları Kullanılarak Sınıflandırılması

Year 2024, Volume: 8 Issue: 2, 133 - 137

Fırat Orhanbulucu , Fatma Latifoğlu , Ayşegül Güven , Semra İçer , Aigul Zhusupova

Abstract

Bu çalışmada 12 kanallı elektrokardiyogram (EKG) sinyalleri kullanılarak miyokard enfarktüsü (MI) ve diğer koroner kalp hastalıklarının tanısı için bir yaklaşım sunulmaktadır. Sunulan yaklaşımda Erciyes Üniversitesi Hastanesi Acil Servisine kalp rahatsızlığı nedeniyle başvuran MI tipleri (STEMI-NSTEMI), diğer kalp hastalıkları (OHD) ve sağlıklı kontrol (SK) katılımcılarının 12 kanallı EKG sinyalleri kayıtları kullanılmıştır. İlk aşamada, gürültüden arındırılmış EKG sinyalleri Varyasyonel Mod Ayrıştırma (VMD) yöntemi uygulanarak alt bantlara ayrıştırılmış ve kinetik özellikler elde edilmiş, sınıflandırıcıların performansını olumlu yönde etkileyecek olanlar Ki-kare testi ile belirlenmiştir. Sınıflandırma aşamasında bu özellikler Destek Vektör Makinesi (SVM), Rastgele Orman (RF) ve Yapay Sinir Ağı (YSA) algoritmaları ile değerlendirilmiş ve AUC, Doğruluk ve Negatif Tahmini Değer oranları elde edilmiştir. HC-OHD, HC-MI (NSTEMI+STEMI) ve STEMI-NSTEMI-OHD grupları için sınıflandırma işlemleri gerçekleştirildi. AUC açısından değerlendirildiğinde başarılı sayılabilecek oranlar (%80 ve üzeri) elde edildi. Bu araştırmanın bulguları, manuel olarak yorumlanması zor olabilen EKG sinyallerinden koroner kalp hastalıklarının hızlı ve doğru tanısı için geliştirilebilecek sistemlere katkıda bulunabilir.

Keywords

Koroner kalp hastalığı, 12 derivasyonlu elektrokardiyogram (EKG) sinyali, Kinetik özellikler, Varyasyonel mod ayrıştırma, Makine öğrenimi algoritmaları

Project Number

TÜSEB 20116

Analysis of Coronary Heart Diseases by Kinetic Features: Applying Variational Mode Decomposition to ECG Signals and Classification Using Machine Learning Algorithms

Abstract

This study presents an approach for the diagnosis of myocardial infarction (MI) and other coronary heart diseases using 12-lead electrocardiogram (ECG) signals. In the presented approach, 12-lead ECG signals recordings of MI types (STEMI-NSTEMI), other heart diseases (OHD) and healthy control (HC) participants, who presented to the Emergency Department of Erciyes University Hospital for heart disease, were used. In the first stage, the noise-cleaned ECG signals were decomposed into subbands by applying the Variational Mode Decomposition (VMD) method and kinetic features were obtained, and the ones that would positively affect the performance of the classifiers were determined by Chi-square test. In the classification stage, these features were evaluated by Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) algorithms, and AUC, Accuracy, and Negative Predictive Value ratios were obtained. Classification procedures were performed for HC-OHD, HC-MI (NSTEMI+STEMI), and STEMI-NSTEMI-OHD groups. When evaluated in terms of AUC, rates that can be considered successful (80% and above) were obtained. The findings of this research may contribute to the systems that can be developed for the rapid and accurate diagnosis of coronary heart diseases from ECG signals, which can be difficult to interpret manually.

Keywords

Coronary heart disease, 12-lead electrocardiogram (ECG) signal, Kinetic features, Variational mode decomposition, Machine learning algorithms

Project Number

TÜSEB 20116

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