ArticlemHealth2025
Performance of a medical smartband with photoplethysmography technology and artificial intelligence algorithm to detect atrial fibrillation.
Article in mHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- Quantum Angle-distance kernel for ECG classification and anomaly detection: a quantum-inspired framework for biomedical signal analysis.BioData mining · 2026Article
- Photoplethysmography acceleration indices as digital biomarkers of cardiometabolic health.Frontiers in digital health · 2026Article
- Artificial intelligence in atrial fibrillation - Timely diagnosis, risk assessment and personalized management.Indian pacing and electrophysiology journalReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Atrial fibrillation (AF) is a prevalent arrhythmia with significant public health implications, including increased risk of stroke and mortality. Early detection is challenging but crucial for managing complications. Wearable technology with photoplethysmography (PPG) offers a potential solution for long-term, non-invasive monitoring. This study aims to evaluate the performance of three artificial intelligence (AI) algorithms (Happitech, Preventicus, and Philips Biosensing AF) in detecting AF using PPG signals from a medical smartband and compare it with the gold standard electrocardiogram (ECG). Methods: A medical smartband equipped with PPG technology was used to collect cardiovascular data from patients with and without AF. The sensitivity and specificity of the algorithm for detecting AF were determined by comparing their output to a trained technician's examination of concurrent ECG recordings. Results: Seventy two participants (42% female, 57±17 years old) were included in this study. The medical smartband provided continuous PPG signals, with AI algorithms evaluating the data for AF episodes. The accuracy of AF detection by the algorithms was compared with that of the concurrent ECG recordings. Sensitivity varied between 80.0% (62.5-97.5%) and 97.6% (97.6-97.6%), specificity between 90.6% (80.5-100%) and 96.9% (90.8-100%). Conclusions: This study demonstrates the potential of medical smartbands combined with PPG technology and AI algorithms for reliable AF detection. The findings suggest a promising direction for remote AF monitoring and early intervention, potentially reducing AF-related complications and healthcare costs.
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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 Socratic graph.