ArticleBMC medical informatics and decision making2021
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Identification of atrial fibrillation using heart rate variability: a meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Clinically-aligned explainable AI for atrial fibrillation detection: A U-Net inspired multi-lead ECG analysis framework.Computer methods and programs in biomedicine · 2026Article
- Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.JMIR cardio · 2026Article
- Long-term continuous monitoring of wearable ECG signals for atrial fibrillation detection.Scientific reports · 2026Article
- Reliable ECG classification using parallel hybrid models with limited resources.Scientific reports · 2026Article
- A joint CNN-Bi-LSTM-transformer architecture with SHAP explanations for multi-label arrhythmia detection from 12-lead ECGs.Scientific reports · 2026Article
- Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis.NPJ digital medicine · 2025Article
- An improved electrocardiogram arrhythmia classification performance with feature optimization.BMC medical informatics and decision making · 2024Article
- Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data.Sensors (Basel, Switzerland) · 2024Article
- Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet.Frontiers in physiology · 2024Article
- 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
- 2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries.Sensors (Basel, Switzerland) · 2023Article
- Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.Sensors (Basel, Switzerland) · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Atrioventricular Synchronization for Detection of Atrial Fibrillation and Flutter in One to Twelve ECG Leads Using a Dense Neural Network Classifier.Sensors (Basel, Switzerland) · 2022Article
- Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.Sensors (Basel, Switzerland) · 2022Article
- Artificial intelligence for the detection, prediction, and management of atrial fibrillation.Herzschrittmachertherapie & Elektrophysiologie · 2022Review
- Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification.PeerJ. Computer science · 2022Article
- DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection.PloS one · 2022Article
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8 authors.
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Abstract
backgroundGeneralization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R-R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity.
resultMeanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity.
conclusionThese findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment.
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