Cardiovascular diseases are one of the leading causes of mortality worldwide, and arrhythmia detection is crucial for early diagnosis. In this study, an explainable and computationally efficient two-stage hybrid learning model is proposed for arrhythmia detection from ECG (Electrocardiogram) signals. The proposed framework combines classical statistical feature extraction methods (wavelet, Welch, RR interval), a CatBoost-based explainable classifier, and a Mini 1D-CNN-based deep learning model with a decision-level soft-weighted fusion strategy. In the first stage, the model successfully distinguished normal and arrhythmic beats with 95.37% accuracy and an AUC of 0.9866. In the second stage, arrhythmic subtypes (L, R, V, A) were classified with 91.24% accuracy, Macro F1 = 0.90, and Macro AUC = 0.971. The CatBoost component provides statistical generalization power and explainability, while the Mini 1D-CNN effectively learned structural patterns in heart waveforms (P, QRS, T), increasing morphological sensitivity. The results show that the proposed method achieves similar accuracy levels with six times fewer parameters compared to complex deep hybrid models and is applicable in real-time systems with low computational cost.
This study is considered an innovative step toward the development of explainable AI-based cardiac diagnostic systems.
ECG Arrhythmia Classification CatBoost Hybrid Learning Explainable Artificial Intelligence (XAI) Two-Stage Model
ECG, Arrhythmia Classification, CatBoost, Hybrid Learning, Explainable Artificial Intelligence (XAI), Two-Stage Model
ECG Aritmi Sınıflandırması CatBoost Hibrit Öğrenme Açıklanabilir Yapay Zekâ (XAI) İki Aşamalı Model
| Birincil Dil | İngilizce |
|---|---|
| Konular | Karar Desteği ve Grup Destek Sistemleri |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Gönderilme Tarihi | 3 Şubat 2026 |
| Kabul Tarihi | 23 Mart 2026 |
| Yayımlanma Tarihi | 30 Mart 2026 |
| DOI | https://doi.org/10.46810/tdfd.1880418 |
| IZ | https://izlik.org/JA44KX47RC |
| Yayımlandığı Sayı | Yıl 2026 Cilt: 15 Sayı: 1 |
Bu eser Creative Commons Atıf-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.