Key result
Batch-ensemble and packed-ensemble outperform nine other uncertainty quantification methods for deep learning-based AF detection.
Why the study?
DL models for AF detection are susceptible to overfitting, yet exploration and comprehensive comparison of uncertainty quantification methods using Holter ECG recordings have been limited.
Which uncertainty quantification methods provide the best performance and computational efficiency for deep learning-based atrial fibrillation detection from Holter recordings?
Which uncertainty quantification methods provide the best performance and computational efficiency for deep learning-based atrial fibrillation detection from Holter recordings?
Batch-ensemble and packed-ensemble methods offer superior uncertainty quantification for deep learning models detecting atrial fibrillation from Holter recordings, balancing high performance with computational efficiency.
Answers are grounded in this paper and cite their sources.
May improve DL reliability for Holter AF detection; leaves open prospective validation and clinical impact.
With the development of deep learning (DL)-based methods, automated atrial fibrillation (AF) detection from electrocardiograms (ECGs) has recently gained much attention. Although the performance of DL has been encouraging, the susceptibility of DL models to overfitting would benefit from the exploration of uncertainty quantification (UQ) to ensure safe integration into clinical practice. However, there has been limited exploration of UQ methods in the context of DL models for AF detection using Holter ECG recordings, and a comprehensive comparison of various UQ techniques remains absent. This study addressed this gap by introducing a benchmark study wherein 11 distinct UQ methods were rigorously evaluated and compared across three public Holter repositories: IRIDIA-AF, Long-Term AF, and MIT-BIH AF datasets. A residual DL model was used for the UQ methods, which is one of the most common architectures in this domain for its ability to capture complex patterns within ECG data. The findings revealed that batch-ensemble (BE) and packed-ensemble (PE) outperformed other UQ methods concerning both performance, as quantified by sensitivity, specificity and expected calibration error, and computational efficiency. In addition, when we implemented reject inference to discard ECG segments where the model confidence was not sufficiently high, BE and PE still showed to reject the least number of samples, while retaining the highest detection performance.
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Rahman et al. (2025) studied Atrial fibrillation. Uncertainty quantification (UQ) methods (11 distinct methods) vs. Comparison among the 11 UQ methods was evaluated on Performance quantified by sensitivity, specificity, expected calibration error, and computational efficiency. Batch-ensemble and packed-ensemble outperformed 9 other uncertainty quantification methods for deep learning-based atrial fibrillation detection in sensitivity, specificity, and calibration error.
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