ArticleSensors (Basel, Switzerland)2021
Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
- Physiological Monitoring Applications of Wearable Multimodal Fusion Systems Based on ECG and PPG: A Comprehensive Review.Sensors (Basel, Switzerland) · 2026Review
- Analysis framework for higher-order temporal correlations with applications to human heartbeats.Scientific reports · 2026Article
- Enhancing ECG Classification Generalization Through Unified Multi-Dataset Training.Sensors (Basel, Switzerland) · 2026Article
- 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
- Association between ultra-short-term heart rate variability of time fluctuation and atrial fibrillation: Evidence from MIMIC-IV.Heart rhythm O2 · 2025Article
- Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.PloS one · 2025Article
- Heart disease detection using an acceleration-deceleration curve-based neural network with consumer-grade smartwatch data.Heliyon · 2024Article
- [Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Photoplethysmography based atrial fibrillation detection: a continually growing field.Physiological measurement · 2024Review
- Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.Journal of Korean medical science · 2024Article
- Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure.Nature communications · 2023Article
- A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG.Diagnostics (Basel, Switzerland) · 2023Article
- Wearables in Cardiovascular Disease.Journal of cardiovascular translational research · 2023Review
- Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.Sensors (Basel, Switzerland) · 2023Review
- Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces.Sensors (Basel, Switzerland) · 2023Article
- A training pipeline of an arrhythmia classifier for atrial fibrillation detection using Photoplethysmography signal.Frontiers in physiology · 2023Article
- Special Issue "Computer Aided Diagnosis Sensors".Sensors (Basel, Switzerland) · 2022Article
- Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.
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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.