Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection
This work identifies ECG sampling frequency as an underappreciated factor affecting deep learning-based arrhythmia detection, providing actionable guidelines for model training and dataset curation.
The study systematically benchmarks deep learning models for atrial fibrillation detection across ECG sampling frequencies (62-500 Hz), finding that intermediate frequencies (100-250 Hz) yield optimal performance for a hybrid CNN-LSTM model, while a 1-D CNN degrades at 500 Hz. The results highlight sampling frequency as a critical factor for clinical reliability.
Deep learning models for atrial fibrillation (AF) detection are increasingly trained on heterogeneous electrocardiogram (ECG) datasets with varying sampling frequencies, yet the specific consequences of these discrepancies on model performance, calibration, and robustness remain insufficiently characterized. To address this, we conducted a systematic benchmark using 12-lead, 10-second recordings from the PTB-XL dataset, resampled to target frequencies of 62, 100, 250, and 500 Hz, to evaluate a standard 1-D Convolutional Neural Network (CNN) and a hybrid CNN-Long Short-Term Memory (LSTM) architecture under a rigorous patient-safe cross-validation framework. Our analysis reveals that sampling frequency significantly impacts detection metrics in an architecture-dependent manner; the hybrid CNN-LSTM model demonstrated optimal performance and consistent calibration at intermediate frequencies (100-250 Hz), whereas the 1-D CNN baseline exhibited marked degradation in accuracy and sensitivity at 500 Hz, suggesting increased susceptibility to high-frequency noise. We conclude that ECG sampling frequency is a critical, underappreciated factor in arrhythmia detection, and future foundation models must explicitly control for temporal resolution to ensure clinical reliability and reproducibility.