Kardiyolojide Doğru Aritmi Sınıflandırması için Derin Öğrenme Modelleri

Year 2026, Volume: 9 Issue: 2, 922 - 945, 16.03.2026

Erhan Kavuncuoglu , Mehmet Akif Buzpınar

https://doi.org/10.47495/okufbed.1734534

https://izlik.org/JA53BZ67RB

Abstract

Kardiyak aritmiler, dünya genelinde kardiyovasküler hastalıklara bağlı mortalitenin önemli bir nedeni olmaya devam etmektedir. Elektrokardiyogram (EKG) verilerinin aritmi sınıflandırması için manuel yorumlanması, doğası gereği öznel ve zaman alıcıdır, bu da doğru ve etkin tanı için zorluklar oluşturur. Derin öğrenmedeki son gelişmeler, EKG analizini otomatikleştirme ve tanısal hassasiyeti artırma konusunda büyük umut vaat etmektedir. Bu çalışma, yaygın olarak kullanılan MIT-BIH Aritmi verisetini kullanarak çeşitli derin öğrenme mimarilerinin performansını değerlendirmektedir. Bulgularımız, ConvLSTM modelinin literatürdeki benzerlerinden çok daha başarılı olarak test setinde %98,81’lik üstün bir doğruluk elde ettiğini göstermektedir. Ayrıca, CNN modeli normal kalp atışlarını etkili bir şekilde tanımlarken, ConvLSTM modeli erken ventriküler kontraksiyonların tespitinde en yüksek performansı göstermektedir. Karmaşık veri ön işleme süreçlerini basitleştirerek, kapsamlı manuel özellik mühendisliği ihtiyacını ortadan kaldırarak otomatik özellik çıkarma imkânı sağlayan derin öğrenme modelleri, aritmi tespitinde önemli bir avantaj sağlamıştır. Bu çalışma, derin öğrenmenin yaygın klinik uygulamalar için potansiyelini vurgulamakta ve hibrit derin öğrenme algoritmalarının gelecekteki tanısal sistemlerde, probleme özgü yüksek performans elde edebileceğini vurgulamaktadır.

Keywords

Kardiyak aritmi tespiti , Derin öğrenme , Tanısal doğruluk , CNN , ConvLSTM

Deep Learning Models for Accurate Arrhythmia Classification in Cardiology

https://izlik.org/JA53BZ67RB

Abstract

Cardiac arrhythmias remain a significant contributor to global cardiovascular disease-related mortality. The manual interpretation of electrocardiogram (ECG) data for arrhythmia classification is inherently subjective and time-intensive, posing challenges for accurate and efficient diagnosis. Recent advancements in deep learning have shown great promise in automating ECG analysis and improving diagnostic precision. This study evaluates the performance of various deep learning architectures using the widely recognized MIT-BIH Arrhythmia Dataset. Our findings demonstrate that the ConvLSTM model achieves superior accuracy, reaching 98.81% on the test set. Moreover, while the CNN model effectively identifies normal heartbeats, the ConvLSTM model exhibits the highest performance in detecting premature ventricular contractions. By simplifying complex data preprocessing, eliminating the need for extensive manual feature engineering, and enabling automatic feature extraction, deep learning models offer a transformative approach to arrhythmia detection. This study highlights the potential of deep learning for widespread clinical implementation and suggests that hybrid deep learning algorithms could achieve high problem-specific performance in future diagnostic systems.

Keywords

Cardiac arrhythmia detection , CNN , ConvLSTM , Deep learning , Diagnostic accuracy

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