ArticleNPJ digital medicine2020
Multi-task deep learning for cardiac rhythm detection in wearable devices.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 of them syntheses 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.
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Who cites it
58 citing papers in PubMed, 3 syntheses or guidelines pooled it, 131 citations in OpenAlex.
- Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2024Pooled it
- Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis.Machine learning. Health · 2026Article
- A multitask Transformer-VAE framework for robust PPG denoising and atrial fibrillation screening.BMC medical imaging · 2026Article
- MIMIC-III-Ext-PPG, a PPG-based Benchmark Dataset for Cardiovascular and Respiratory Signal Analysis.Scientific data · 2026Article
- Multiclass Arrhythmia Classification Using Multimodal Smartwatch Photoplethysmography Signals Collected in Real-Life Settings.IEEE transactions on bio-medical engineering · 2026Article
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Data-hugging shields proprietary AI models from research that could disprove them.NPJ artificial intelligence · 2026Review
- Development of an efficient deep learning model for arrhythmia detection from consumer smartwatches.Frontiers in artificial intelligence · 2026Article
- Diagnostic Accuracy of Wearable ECG Devices for Atrial Fibrillation and ST-Segment Changes: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights.Scientific reports · 2025Article
- Smart Healthcare at Home: A Review of AI-Enabled Wearables and Diagnostics Through the Lens of the Pi-CON Methodology.Sensors (Basel, Switzerland) · 2025Review
- Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational study.Biomedical engineering online · 2025Observational
- Medical laboratory data-based models: opportunities, obstacles, and solutions.Journal of translational medicine · 2025Review
- Article
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
- Wearable Devices for Arrhythmia Detection: Advancements and Clinical Implications.Sensors (Basel, Switzerland) · 2025Review
- The potential risks of artificial intelligence in cardiovascular care.Turk gogus kalp damar cerrahisi dergisi · 2025Article
- Developing a named entity framework for thyroid cancer staging and risk level classification using large language models.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. The model is trained on approximately one million simulated unlabeled physiological signals and fine-tuned on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of atrial fibrillation detection from a F1 score of 0.54 to 0.96. We also include in our evaluation a prospectively derived replication cohort of ambulatory participants where the algorithm performed with high sensitivity (0.98), specificity (0.99), and F1 score (0.93). We show that two-stage training can help address the unbalanced data problem common to biomedical applications, where large-scale well-annotated datasets are hard to generate due to the expense of manual annotation, data acquisition, and participant privacy.
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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.