An R-R interval-based deep learning model using a 3-minute window achieved 96.9% accuracy for atrial fibrillation classification and highly correlated with reference AF burden (r = 0.988).
Observational (n=117)
No
Does an RRI-based 2D-CNN deep learning model accurately detect atrial fibrillation and estimate AF burden in noisy wearable Holter ECG data?
An RRI-based 2D-CNN deep learning model using a 3-minute window provides highly accurate and noise-robust atrial fibrillation screening from garment-type wearable Holter ECGs.
Effect estimate: r = 0.988
Background: Atrial fibrillation (AF) is a significant contributor to cardioembolic stroke, necessitating early and precise detection of AF to mitigate associated risks. Long-term Holter electrocardiography (ECG) monitoring using garment-type wearable devices produces large volumes of single-lead data with various noise artifacts. Deep learning has achieved high performance in AF detection from ECG data; however, many deep learning studies report strong performance on curated datasets or noise-controlled recordings. Comparatively fewer approaches have been developed and evaluated with an explicit strategy to maintain diagnostic accuracy in noise-included real-world wearable Holter ECG data. An alternative representation using the R–R interval (RRI) time series may reduce the dependence on waveform morphology and provide a computationally efficient pathway for robust AF screening in noisy recordings. Objective: This study aims to develop a computationally efficient, noise-robust deep learning model that leverages the irregularity of the RRI in noisy wearable monitoring environments. We evaluated the impact of the analysis window length on model performance. Methods: Single-lead Holter ECG data from 117 patients at the University of Osaka Hospital were analyzed, excluding those with atrial tachycardia/flutter. The RRIs were extracted, segmented into 1.5-, 3-, and 6-min windows, and transformed into two-dimensional histogram images. A ResNet-34–based two-dimensional convolutional neural network (2D-CNN) was trained for three-class classification. The model performance was evaluated using five-fold inter-patient cross-validation and externally validated using the MIT-BIH AF Database. Patient-level AF burden was defined as the proportion of AF duration relative to total analyzable recording time per patient; agreement between cardiologist-derived and model-estimated AF burden was assessed using Pearson’s correlation coefficient and linear regression. Results: Of 129 monitored patients (Feb 1, 2023–Nov 20, 2025), 117 were analyzed. In the internal validation, the 3-min window had superior performance (accuracy, 96.9%; AF sensitivity, 97.0%; AF specificity, 98.2%). External validation corroborated this balance (accuracy, 96.1%; AF sensitivity, 93.3%; and AF specificity, 98.7%). The 3-min model exhibited an exceptionally high correlation with the reference AF burden (r = 0.988, R² = 0.976). Conclusions: The RRI-based 2D-CNN achieved high AF classification accuracy and excellent agreement with AF burden. By utilizing RRI features and a noise-adaptive training strategy, a 3-min RRI window has emerged as a practical solution for efficient AF screening in a garment-type Holter ECG.
Nakano et al. (Thu,) conducted a observational in Atrial fibrillation (n=117). R-R interval histogram-based deep learning model (ResNet-34 2D-CNN) vs. Cardiologist-derived reference was evaluated on AF classification accuracy and agreement with AF burden (r = 0.988). An R-R interval-based deep learning model using a 3-minute window achieved 96.9% accuracy for atrial fibrillation classification and highly correlated with reference AF burden (r = 0.988).