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Updated: Aug 15, 2026
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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
RR-interval-based atrial fibrillation detection and burden estimation: cross-dataset validation and calibration-aware
Mohammad Haekal1, Siti Nurul Khotimah2, Galih Restu Fardian Suwandi2
1Physics, Institut Teknologi Bandung Fakultas Matematika dan Ilmu Pengetahuan Alam, Jl. Ganeca 10, Bandung, 40132, Indonesia.
Physiological Measurement
|August 13, 2026
Summary
Accurate atrial fibrillation (AF) burden estimation requires calibrated AF detection probabilities. Recalibration significantly improved probability accuracy and burden estimation in external datasets, highlighting its importance for reliable AF burden assessment.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning in Healthcare
Background:
- Atrial fibrillation (AF) burden is a critical endpoint in long-term rhythm monitoring.
- Reliable AF burden estimation depends not only on accurate AF detection but also on probability calibration, especially under external dataset shifts.
- Existing methods may face challenges in maintaining burden validity when predicted AF probabilities are aggregated over time.
Purpose of the Study:
- To develop an interpretable RR-interval feature model for AF detection.
- To evaluate the model's performance, including probability calibration, on development and independent external datasets.
- To assess the impact of probability calibration on AF burden estimation derived from aggregated probabilities.
Main Methods:
- Developed an RR-interval feature model for AF detection.
- Evaluated the model using record-wise cross-validation and independent external validation on public Holter ECG databases.
- Assessed window-level performance using ROC-AUC, PR-AUC, Brier score, ECE, and calibration metrics; estimated recording-level AF burden using probability-based and hard-label aggregation.
Main Results:
- The model demonstrated high discrimination (external ROC-AUC: 0.9868, PR-AUC: 0.9872) but showed deteriorated external calibration (ECE(15): 0.1219).
- Probability-based AF burden estimation showed strong association with reference burden but weaker agreement than hard-label aggregation (MAE: 0.1473 vs. 0.0836), indicating systematic probability underprediction.
- External recalibration (Platt and isotonic) substantially improved probability quality (median ECE(15) reduced to 0.0394 and 0.0287) and probability-based burden estimation (median MAE reduced to 0.0692 and 0.0604).
Conclusions:
- RR-interval-based AF detection maintains strong ranking performance across datasets.
- Probability calibration is crucial for the validity of AF burden estimates derived from aggregated probabilities.
- Explicit evaluation and application of recalibration methods are recommended when using predicted probabilities for AF burden estimation.