ArticleJACC. Asia2021
Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Prediction: A Report From the Huawei Heart Study.
Article in JACC. Asia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 28 citations in OpenAlex.
- Vagal Nerve Stimulation for Cardiovascular Diseases: From Hype to Hope.Circulation · 2026Article
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- Wearable-measured heart rate variability and premenstrual disorder symptoms across menstrual cycle.Archives of women's mental health · 2026Article
- Beyond burden metrics: Wearable photoplethysmography-derived spatiotemporal progression of atrial fibrillation linked to clinical outcomes.Heart rhythm O2 · 2026Article
- Artificial Intelligence-driven Detection, Mapping, and Personalized Therapy for Atrial Fibrillation.The Journal of innovations in cardiac rhythm management · 2026Review
- A comprehensive guide to digital neurology. An update on current development on digital neurology derived from the Digital Neuro Hub project.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation.JMIR cardio · 2025Article
- Short-term atrial fibrillation onset prediction using machine learning.European heart journal. Digital health · 2025Article
- Integrating wearables into the modern electrophysiology practice: A proposed framework for the use of wearables and creation of a remote digital clinic.Heart rhythm O2 · 2025Article
- Revolutionizing Stroke Management with Wearable Technology: A Narrative Review of Clinical Applications, Opportunities, Challenges, and the Future Path Towards Equitable Implementation.Annals of Indian Academy of Neurology · 2025Article
- The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation.Cureus · 2025Review
- Heart disease detection using an acceleration-deceleration curve-based neural network with consumer-grade smartwatch data.Heliyon · 2024Article
- A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation.Journal of personalized medicine · 2024Article
- Global voices on atrial fibrillation care in China.Heart rhythm O2 · 2024Article
- Early warning of atrial fibrillation using deep learning.Patterns (New York, N.Y.) · 2024Article
- Photoplethysmography based atrial fibrillation detection: a continually growing field.Physiological measurement · 2024Review
- [Artificial intelligence in wearable electrocardiogram monitoring].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- [Application of photoplethysmography for atrial fibrillation in early warning, diagnosis and integrated management].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Yield of diagnosis and risk of stroke with screening strategies for atrial fibrillation: a comprehensive review of current evidence.European heart journal open · 2023Review
- Machine Learning and the Conundrum of Stroke Risk Prediction.Arrhythmia & electrophysiology review · 2023Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Current wearable devices enable the detection of atrial fibrillation (AF), but a machine learning (ML)-based approach may facilitate accurate prediction of AF onset. Objectives: The present study aimed to develop, optimize, and validate an ML-based model for real-time prediction of AF onset in a population at high risk of incident AF. Methods: A primary ML-based prediction model of AF onset (M1) was developed on the basis of the Huawei Heart Study, a general-population AF screening study using photoplethysmography (PPG)-based smart devices. After optimization in 554 individuals with 469,267 PPG data sets, the optimized ML-based model (M2) was further prospectively validated in 50 individuals with paroxysmal AF at high risk of AF onset, and compared with 72-hour Holter electrocardiographic (ECG) monitoring, a criterion standard, from September 1, 2019, to November 5, 2019. Results: Among 50 patients with paroxysmal AF (mean age 67 ± 12 years, 40% women), there were 2,808 AF events from a total of 14,847,356 ECGs over 72 hours and 6,860 PPGs (45.83 ± 13.9 per subject per day). The best performance of M1 for AF onset prediction was achieved 4 hours before AF onset (area under the receiver operating characteristic curve: 0.94; 95% confidence interval: 0.93-0.94). M2 sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (at 0 to 4 hours before AF onset) were 81.9%, 96.6%, 96.4%, 83.1%, and 88.9%, respectively, compared with 72-hour Holter ECG. Conclusions: The PPG- based ML model demonstrated good ability for AF prediction in advance. (Mobile Health [mHealth] technology for improved screening, patient involvement and optimizing integrated care in atrial fibrillation; ChiCTR-OOC-17014138).
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