ArticleComputers in biology and medicine2020
HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks.
Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2024Pooled it
- Automated analysis of ECG signals using nonlinearity and nonstationarity features fed into the MobilenetV2 CNN powered by transfer learning.Physical and engineering sciences in medicine · 2025Article
- Detection of Atrial Fibrillation in Holter ECG Recordings by ECHOView Images: A Deep Transfer Learning Study.Diagnostics (Basel, Switzerland) · 2025Article
- Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation.Biophysics reviews · 2025Review
- An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.JMIR human factors · 2025Article
- Clinically interpretable multiclass neural network for discriminating cardiac diseases.Heliyon · 2025Article
- mRMEBP: a unified framework for online detection of atrial fibrillation utilizing deep learning.npj biomedical innovations · 2024Article
- [Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.Scientific reports · 2024Article
- ECG autoencoder based on low-rank attention.Scientific reports · 2024Article
- Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.Current cardiology reports · 2024Review
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives.Frontiers in cardiovascular medicine · 2024Review
- Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- CatBoost-based improved detection of P-wave changes in sinus rhythm and tachycardia conditions: a lead selection study.Physical and engineering sciences in medicine · 2023Article
- Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model.Journal of personalized medicine · 2023Article
- A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection.Sensors (Basel, Switzerland) · 2023Article
- Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation.Sensors (Basel, Switzerland) · 2023Article
- Explainable AI for clinical and remote health applications: a survey on tabular and time series data.Artificial intelligence review · 2023Article
- Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
Abstract
Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of many people around the world and is associated with a five-fold increased risk of stroke and mortality. Like other problems in the healthcare domain, artificial intelligence (AI)-based models have been used to detect AF from patients' ECG signals. The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability. In other words, these approaches are unable to explain the reasons behind their decisions. The lack of interpretability is a common challenge toward a wide application of machine learning (ML)-based approaches in the healthcare which limits the trust of clinicians in such methods. To address this challenge, we propose HAN-ECG, an interpretable bidirectional-recurrent-neural-network-based approach for the AF detection task. The HAN-ECG employs three attention mechanism levels to provide a multi-resolution analysis of the patterns in ECG leading to AF. The detected patterns by this hierarchical attention model facilitate the interpretation of the neural network decision process in identifying the patterns in the signal which contributed the most to the final detection. Experimental results on two AF databases demonstrate that our proposed model performs better than the existing algorithms. Visualization of these attention layers illustrates that our proposed model decides upon the important waves and heartbeats which are clinically meaningful in the detection task (e.g., absence of P-waves, and irregular R-R intervals for the AF detection task).
Indexed as
Identifiers
What OpenQuestion holds
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.