# Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1805.09133 · HTML: https://mlsanity.com/paper/1805.09133/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 9 of 9)

- [Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals](https://mlsanity.com/paper/2210.11408/) (2022)
- [MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring](https://mlsanity.com/paper/2301.12178/) (2023)
- [LLM-ABBA: Understanding time series via symbolic approximation](https://mlsanity.com/paper/2411.18506/) (2024)
- [Deep conv-attention model for diagnosing left bundle branch block from 12-lead electrocardiograms](https://mlsanity.com/paper/2212.04936/) (2022)
- [Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search](https://mlsanity.com/paper/2301.10173/) (2023)
- [Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal](https://mlsanity.com/paper/2308.02433/) (2023)
- [Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG](https://mlsanity.com/paper/2101.07535/) (2021)
- [MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation](https://mlsanity.com/paper/2302.03731/) (2023)
- [Patient-Aware Contrastive Learning Preserves Per-Patient Structure in RR-Interval Representations](https://mlsanity.com/paper/2606.23570/) (2026)

## Abstract (arXiv)

Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability of being undetected. In this work, we present an attention based deep learning framework for detection of paroxysmal AF episodes from a sequence of windows. Time-frequency representation of 30 seconds recording windows, over a 10 minute data segment, are fed sequentially into a deep convolutional neural network for image-based feature extraction, which are then presented to a bidirectional recurrent neural network with an attention layer for AF detection. To demonstrate the effectiveness of the proposed framework for transient AF detection, we use a database of 24 hour Holter Electrocardiogram (ECG) recordings acquired from 2850 patients at the University of Virginia heart station. The algorithm achieves an AUC of 0.94 on the testing set, which exceeds the performance of baseline models. We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance. The proposed high accuracy, low false alarm algorithm for detecting paroxysmal AF has potential applications in long-term monitoring using wearable sensors.