ReviewNPJ digital medicine2020
Photoplethysmography based atrial fibrillation detection: a review.
Review 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 130 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
130 citing papers in PubMed, 1 synthesis or guideline pooled it, 282 citations in OpenAlex.
- Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network.British journal of sports medicine · 2021Guideline
- Demographic subgroup-aware analysis of blood pressure predictive models.PLOS digital health · 2026Article
- Mechanical Contact Conditions in Wearable Reflectance Photoplethysmography: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Atrial Fibrillation Burden: Assessment, Clinical Significance, and Therapeutic Implications.Life (Basel, Switzerland) · 2026Review
- Neuromorphic Cardiac Sensing: A Bio-Inspired Spiking Neural Network with Sensory-Adaptive Encoding for Energy-Efficient Arrhythmia Detection from ECG and PPG Signals.Biomimetics (Basel, Switzerland) · 2026Article
- Structure-Controlled Cyclopentadithiophene Derived Non-Fullerene Acceptors for Efficient Near-Infrared Organic Photodetectors.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- A multitask Transformer-VAE framework for robust PPG denoising and atrial fibrillation screening.BMC medical imaging · 2026Article
- Prediction of vasovagal syncope using artificial intelligence-enabled smartwatch photoplethysmography-derived heart rate variability.European heart journal. Digital health · 2026Article
- Participant-Aware Model Validation for Repeated-Measures Data: Comparative Cross-Validation Study.JMIR AI · 2026Article
- Multi-Class Arrhythmia Detection from PPG Signals Based on VGG-BiLSTM Hybrid Deep Learning Model.Biosensors · 2026Article
- Vascular waveform analysis using Bayesian pulse deconvolution.bioRxiv : the preprint server for biology · 2026Article
- Analysis of differential photoplethysmography signal patterns in apnea and hypopnea.Physiological measurement · 2026Article
- Atrial Fibrillation Detection from At-Rest PPG Signals Using an SDOF-TF Method.Sensors (Basel, Switzerland) · 2026Article
- Privacy-preserving adaptive arrhythmia monitoring with wearable ECG, smartphone PPG, and federated on-device learning.Frontiers in digital health · 2026Article
- Multi class photoplethysmography-based deep model for cardiovascular disease classification.PloS one · 2026Article
- Development of an efficient deep learning model for arrhythmia detection from consumer smartwatches.Frontiers in artificial intelligence · 2026Article
- Exploring nurse- and allied health professional-led opportunistic atrial fibrillation screening with artificial intelligence-enabled devices in community and primary care.Frontiers in cardiovascular medicine · 2026Review
- Observational
- From Detection to Decision: Managing Device-Detected Atrial Fibrillation After Stroke.Current neurology and neuroscience reports · 2025Review
- Machine Learning and Arrhythmia: Advances in Atrial Fibrillation Detection and Management.Current atherosclerosis reports · 2025Review
70 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.
Indexed as
Identifiers
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.