ArticleSensors (Basel, Switzerland)2025
Atrial Fibrillation and Atrial Flutter Detection Using Deep Learning.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development and validation of a LightGBM-based model with risk stratification for predicting early recurrence after catheter ablation for atrial fibrillation.BMC cardiovascular disorders · 2026Article
- AI based ECG data recovery and cardiovascular diseases classification (CEDRC-network).Scientific reports · 2026Article
- Atrial Fibrillation Detection from At-Rest PPG Signals Using an SDOF-TF Method.Sensors (Basel, Switzerland) · 2026Article
- Motion Artifacts Removal from Measured Arterial Pulse Signals at Rest: A Generalized SDOF-Model-Based Time-Frequency Method.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce a lightweight 1D ConvNeXtV2-based neural network for the robust detection of atrial fibrillation (AFib) and atrial flutter (AFL) from single-lead ECG signals. Trained on multiple public datasets (Icentia11k, CPSC-2018/2021, LTAF, PTB-XL, PCC-2017) and evaluated on MIT-AFDB, MIT-ADB, and NST, our model attained a state-of-the-art F1-score of 0.986 on MIT-AFDB. With only 770 k parameters and 46 MFLOPs per 10 s window, the network remained computationally efficient. Guided Grad-CAM visualizations confirmed attention to clinically relevant P-wave morphology and R-R interval irregularities. This interpretable architecture is, therefore, well-suited for deployment in resource-constrained wearable or bedside monitors. Future work will extend this framework to multi-lead ECGs and a broader spectrum of arrhythmias.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.