ArticlePhysiological measurement2018
ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network.
Article in Physiological measurement, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03662802 (Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms), which is not on this map. Cited by 43 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms
Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 160 citations in OpenAlex.
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Wearable Devices and Machine Learning in Cardiovascular Monitoring: Current Evidence and Future Directions for Precision Medicine.Journal of personalized medicine · 2026Review
- Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.JMIR cardio · 2026Article
- Higher-dimensional embedding of time-series data for machine learning.Frontiers in artificial intelligence · 2026Article
- Metabolic Syndrome Detection Based on Classification of Electrocardiography Signals.Sensors (Basel, Switzerland) · 2025Article
- Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.JACC. Asia · 2025Article
- Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.BMC medical informatics and decision making · 2025Article
- ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.Computers in biology and medicine · 2024Article
- Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.Current cardiology reports · 2024Review
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Joint spatio-temporal features constrained self-supervised electrocardiogram representation learning.Biomedical engineering letters · 2024Article
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology.IEEE transactions on neural networks and learning systems · 2023Review
- Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection.Scientific reports · 2023Article
- Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.Sensors (Basel, Switzerland) · 2023Article
- CardioLabelNet: An uncertainty estimation using fuzzy for abnormalities detection in ECG.Health care science · 2023Review
- Article
- Multiscale Encoding of Electrocardiogram Signals with a Residual Network for the Detection of Atrial Fibrillation.Bioengineering (Basel, Switzerland) · 2022Article
- Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography.American heart journal plus : cardiology research and practice · 2022Article
- Objectification of intracochlear electrocochleography using machine learning.Frontiers in neurology · 2022Article
- How synergy between mechanistic and statistical models is impacting research in atrial fibrillation.Frontiers in physiology · 2022Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveThe electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the general population in industrialized countries. Automatic AF detection in clinics remains a challenging task due to the high inter-patient variability of ECGs, and unsatisfactory existing approaches for AF diagnosis (e.g. atrial or ventricular activity-based analyses). APPROACH: We have developed RhythmNet, a 21-layer 1D convolutional recurrent neural network, trained using 8528 single-lead ECG recordings from the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge, to classify ECGs of different rhythms including AF automatically. Our RhythmNet architecture contained 16 convolutions to extract features directly from raw ECG waveforms, followed by three recurrent layers to process ECGs of varying lengths and to detect arrhythmia events in long recordings. Large 15 × 1 convolutional filters were used to effectively learn the detailed variations of the signal within small time-frames such as the P-waves and QRS complexes. We employed residual connections throughout RhythmNet, along with batch-normalization and rectified linear activation units to improve convergence during training. MAIN
resultsWe evaluated our algorithm on 3658 testing data and obtained an F SIGNIFICANCE: Potentially, our approach could aid AF diagnosis in clinics and be used for patient self-monitoring to improve the early detection and effective treatment of AF.
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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.