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Journals / Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 9 Sayı: 4

AI-Enabled Atrial Fibrillation Detection, Prediction, and Classification Based on ECG Data: A Review

Elektrokardiyogram (EKG) Verilerine Dayalı Yapay Zeka Destekli Atrial Fibrilasyonun Tespiti, Tahmini ve Sınıflandırılması Üzerine Bir Derleme

Kimia Tahvildari(BAHCESEHIR UNIVERSITY)
Fatih Kahraman(BAHCESEHIR UNIVERSITY)0000-0002-8393-3425
Pages
2620–2647
Publication Date
2026-09-09
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

While AI holds transformative potential for AF diagnosis and management, significant technical and clinical challenges remain for its widespread adoption in healthcare.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Atrial Fibrillation (AF) is a prevalent and serious cardiac arrhythmia associated with significant health risks, including stroke and heart failure.
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Key Findings & Data:This review systematically examines AI-based approaches for AF analysis, evaluating their advantages and limitations using key performance metrics, including accuracy, sensitivity, specificity, and F-score.
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Conclusion & Impact:While AI holds transformative potential for AF diagnosis and management, significant technical and clinical challenges remain for its widespread adoption in healthcare.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Atriyal Fibrilasyon · Yapay Zeka · Derin Öğrenme

Abstract

Atrial Fibrillation (AF) is a prevalent and serious cardiac arrhythmia associated with significant health risks, including stroke and heart failure. Traditional AF detection using electrocardiogram (ECG) recordings relies on manual interpretation, which is time-consuming and prone to human error. Advances in artificial intelligence (AI) have enabled automated AF detection, prediction, and classification, leveraging deep learning models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, alongside traditional machine learning classifiers like Random Forest (RF). This review systematically examines AI-based approaches for AF analysis, evaluating their advantages and limitations using key performance metrics, including accuracy, sensitivity, specificity, and F-score. Additionally, it discusses challenges such as data variability, sensor limitations, and the integration of AI into clinical workflows. The latest advancements in hybrid models and domain adaptation techniques are explored, addressing key constraints related to model generalizability and data quality. Future research directions emphasize the importance of explainable AI, continuous monitoring through wearable technologies, and clinical validation through real-world trials. While AI holds transformative potential for AF diagnosis and management, significant technical and clinical challenges remain for its widespread adoption in healthcare.

Özet

Atriyal Fibrilasyon (AF), inme ve kalp yetmezliği gibi ciddi sağlık riskleri taşıyan yaygın bir kardiyak aritmi türüdür. Elektrokardiyogram (EKG) kayıtları ile AF tespiti geleneksel olarak manuel gözlem yoluyla gerçekleştirilmekte olup, bu yöntem zaman alıcı ve değişkenlik gösterebilmektedir. Yapay zeka (YZ) ve makine öğrenimi (MÖ) teknolojilerinin gelişmesiyle birlikte, EKG verilerine dayalı AF tespiti, tahmini ve sınıflandırmasını otomatikleştiren ve iyileştiren yeni yaklaşımlar ortaya çıkmıştır. Bu çalışma, AF analizinde kullanılan YZ ve MÖ yöntemlerini sistematik bir şekilde incelemekte olup, Konvolüsyonel Sinir Ağları (CNN) ve Uzun Kısa Süreli Bellek (LSTM) ağları gibi derin öğrenme modellerinin yanı sıra Rastgele Orman (RF) sınıflandırıcıları gibi geleneksel modelleri ele almaktadır. Çalışma, modellerin güçlü ve zayıf yönlerini değerlendirerek doğruluk, duyarlılık, özgüllük ve F-skor gibi temel performans ölçütlerini tartışmaktadır. Ayrıca, veri değişkenliği, sensör sınırlamaları ve YZ'nin klinik iş akışlarına entegrasyonu gibi temel zorluklara dikkat çekmektedir. Hibrit modeller ve alan uyum tekniklerindeki güncel gelişmeler incelenerek, bu yeniliklerin model genelleştirilebilirliği ve veri kalitesine yönelik kısıtlamaları nasıl ele aldığı vurgulanmaktadır. Gelecekteki araştırmalar için açıklanabilir YZ, giyilebilir teknolojilerle sürekli izleme ve klinik deneylerle doğrulama gerekliliği öne çıkarılmaktadır. Bu gelişmeler, YZ araştırmaları ile pratik sağlık hizmeti uygulamaları arasındaki boşluğu kapatmada kritik bir rol oynamaktadır. Bu inceleme, YZ'nin AF teşhis ve yönetimindeki dönüştürücü potansiyelini ortaya koyarken, aynı zamanda mevcut teknik ve klinik zorluklara dikkat çekerek, daha güvenilir ve etkili YZ tabanlı çözümler geliştirilmesine katkı sağlamaktadır.

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Cite This Article

Tahvildari, K., Kahraman, F. (2026). AI-Enabled Atrial Fibrillation Detection, Prediction, and Classification Based on ECG Data: A Review. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 9 Sayı: 4, 2620–2647. https://doi.org/10.47495/okufbed.1677432

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