Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection

TL;DR

This study systematically evaluates how different ECG sampling frequencies affect deep learning-based atrial fibrillation detection, revealing that intermediate frequencies optimize performance and calibration.

Contribution

It provides the first comprehensive benchmark showing the impact of ECG sampling frequency on model accuracy, calibration, and robustness in AF detection.

Findings

Hybrid CNN-LSTM performs best at 100-250 Hz.

High frequency (500 Hz) degrades CNN baseline accuracy.

Sampling frequency critically influences model reliability and reproducibility.

Abstract

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.