# Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework

arXiv: https://arxiv.org/abs/2608.18451 · HTML: https://mlsanity.com/paper/2608.18451/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization](https://mlsanity.com/paper/2509.10140/) (2025)
- [ECGMamba: Towards Efficient ECG Classification with BiSSM](https://mlsanity.com/paper/2406.10098/) (2024)
- [RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG](https://mlsanity.com/paper/2401.05411/) (2023)
- [ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion](https://mlsanity.com/paper/2406.16901/) (2024)
- [Domain Generalization via Discrete Codebook Learning](https://mlsanity.com/paper/2504.06572/) (2025)
- [A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis](https://mlsanity.com/paper/2507.12645/) (2025)
- [A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram](https://mlsanity.com/paper/2204.13917/) (2022)
- [Using Test-Time Data Augmentation for Cross-Domain Atrial Fibrillation Detection from ECG Signals](https://mlsanity.com/paper/2503.13483/) (2025)

## Abstract (arXiv)

Background and Objective: Reliable atrial fibrillation (AF) detection from electrocardiogram (ECG) signals remains challenging in real-world clinical settings due to variable lead configurations, cross-dataset domain shifts, and pervasive physiological and technical artifacts. So we develop a robust and generalizable deep learning model for accurate AF detection. Methods: We propose the Dual-Codebook Graph Collaborative Network (DCGCNet), a novel end-to-end vector-quantized variational autoencoder that jointly performs AF classification and ECG reconstruction. DCGCNet introduces two key components: (1) a Local-Global Contrastive Module for learning noise-invariant representations, and (2) an Adaptive Codebook Vector Quantizer that dynamically refines codebook prototypes to better align with input data distributions, thereby preventing codebook collapse and enhancing generalization. Results: DCGCNet achieves state-of-the-art performance in standard intra-dataset 12-lead evaluation and demonstrates exceptional cross-dataset generalization across seven diverse settings, consistently attaining AUC > 0.98 in all cases. Furthermore, it maintains high diagnostic accuracy under realistic noisy conditions, including baseline wander, powerline interference, and EMG artifacts. Conclusions: DCGCNet establishes a new benchmark for robust, generalizable, and noise-resilient AF detection, showing strong potential for deployment in real-world clinical environments.