ReviewHeart rhythm2020
Deep learning for comprehensive ECG annotation.
Review in Heart rhythm, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Safety, performance, and remote programming utilization of an insertable cardiac monitor: The LUX-Dx PERFORM study.Heart rhythm O2 · 2026Article
- Automated Detection of Normal, Atrial, and Ventricular Premature Beats from Single-Lead ECG Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- Vigilance and Well-Being in Daily Life: An Examination of Race Differences.Race and social problems · 2026Article
- Atrial Fibrillation and Atrial Flutter Detection Using Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes.Journal of medical systems · 2025Article
- Signal-Guided Multitask Learning for Myocardial Infarction Classification Using Images of Electrocardiogram.Cardiology · 2025Article
- Multichannel high noise level ECG denoising based on adversarial deep learning.Scientific reports · 2024Article
- End-to-End Premature Ventricular Contraction Detection Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Episodes of Atrial Fibrillation and Symptoms: A Temporal Analysis.Cardiovascular digital health journal · 2023Article
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
- Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces.Sensors (Basel, Switzerland) · 2023Article
- A fine-tuned YOLOv5 deep learning approach for real-time house number detection.PeerJ. Computer science · 2023Article
- Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.Sensors (Basel, Switzerland) · 2022Review
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Deep-Learning-Based Detection of Paroxysmal Supraventricular Tachycardia Using Sinus-Rhythm Electrocardiograms.Journal of clinical medicine · 2022Article
- How to use digital devices to detect and manage arrhythmias: an EHRA practical guide.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2022Article
- Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study.JMIR medical informatics · 2022Article
- Artificial intelligence for the detection, prediction, and management of atrial fibrillation.Herzschrittmachertherapie & Elektrophysiologie · 2022Review
- Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives.Frontiers in cardiovascular medicine · 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.
Funding
Abstract
backgroundIncreasing utilization of long-term outpatient ambulatory electrocardiographic (ECG) monitoring continues to drive the need for improved ECG interpretation algorithms.
objectiveThe purpose of this study was to describe the BeatLogic® platform for ECG interpretation and to validate the platform using electrophysiologist-adjudicated real-world data and publicly available validation data.
methodsDeep learning models were trained to perform beat and rhythm detection/classification using ECGs collected with the Preventice BodyGuardian® Heart monitor. Training annotations were created by certified ECG technicians, and validation annotations were adjudicated by a team of board-certified electrophysiologists. Deep learning model classification results were used to generate contiguous annotation results, and performance was assessed in accordance with the EC57 standard.
resultsOn the real-world validation dataset, BeatLogic beat detection sensitivity and positive predictive value were 99.84% and 99.78%, respectively. Ventricular ectopic beat classification sensitivity and positive predictive value were 89.4% and 97.8%, respectively. Episode and duration F
conclusionThe BeatLogic platform represents the next stage of advancement for algorithmic ECG interpretation. This comprehensive platform performs beat detection, beat classification, and rhythm detection/classification with greatly improved performance over the current state of the art, with comparable or improved performance over previously published algorithms that can accomplish only 1 of these 3 tasks.
Indexed as
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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.