research-article

Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

Authors:

Alireza Aliamiri,

Andrew NgAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1909 - 1916

https://doi.org/10.1145/3292500.3330657

Published: 25 July 2019 Publication History

Abstract

We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions. We collect and annotate a dataset containing more than 4000 hours of PPG recorded from a wrist-worn device. Using a 50-layer convolutional neural network, we achieve a test AUC of 95% in presence of motion artifacts inherent to PPG signals. Such continuous and accurate detection of AF has the potential to transform consumer wearable devices into clinically useful medical monitoring tools.

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Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

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cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
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Vipin Kumar
University of Minnesota
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Program Chairs:
Ying Li
EV Analysis Corporation
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Rómer Rosales
LinkedIn
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Evimaria Terzi
Boston University
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George Karypis
University of Minnesota

Copyright © 2019 ACM.

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Published: 25 July 2019

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August 4 - 8, 2019

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Zhao LLyu RLei HLin QZhou AMa HWang JMeng XShao CTang YChi GYang Z(2024)AirECG: Contactless Electrocardiogram for Cardiac Disease Monitoring via mmWave Sensing and Cross-domain Diffusion ModelProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785508:3(1-27)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678550
Yang LAmin OShihada B(2024)Intelligent Wearable Systems: Opportunities and Challenges in Health and SportsACM Computing Surveys10.1145/364846956:7(1-42)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.1145/3648469
Zhao LLyu RLin QZhou AZhang HMa HWang JShao CTang Y(2024)mmArrhythmiaProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435498:1(1-25)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643549
Pachori DTripathy RJain T(2024)Detection of Atrial Fibrillation From PPG Sensor Data Using Variational Mode DecompositionIEEE Sensors Letters10.1109/LSENS.2024.33585898:3(1-4)Online publication date: Mar-2024
https://doi.org/10.1109/LSENS.2024.3358589
Ding CGuo ZRudin CXiao RShah ADo DLee RClifford GNahab FHu X(2024)Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia AlarmsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.336095228:5(2650-2661)Online publication date: May-2024
https://doi.org/10.1109/JBHI.2024.3360952
Liu ZZhu TLu LZhang YClifton D(2024)Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography MonitoringIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.334418728:3(1321-1330)Online publication date: Mar-2024
https://doi.org/10.1109/JBHI.2023.3344187
Ding CXiao RWang WHoldsworth EHu X(2024)Photoplethysmography based atrial fibrillation detection: a continually growing fieldPhysiological Measurement10.1088/1361-6579/ad37ee45:4(04TR01)Online publication date: 17-Apr-2024
https://doi.org/10.1088/1361-6579/ad37ee
Liu Y(2024)Survey on Sensor based Fall Assistance System for ElderlyE3S Web of Conferences10.1051/e3sconf/202456502022565(02022)Online publication date: 9-Sep-2024
https://doi.org/10.1051/e3sconf/202456502022
Mohagheghian FHan DGhetia OChen DPeitzsch ANishita NDing EMensah Otabil ENoorishirazi KHamel ADickson EDiMezza DTran KMcManus DChon K(2024)Atrial fibrillation detection on reconstructed photoplethysmography signals collected from a smartwatch using a denoising autoencoderExpert Systems with Applications10.1016/j.eswa.2023.121611237(121611)Online publication date: Mar-2024
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Saad HZaki JAbdelsalam M(2024)Employing of machine learning and wearable devices in healthcare system: tasks and challengesNeural Computing and Applications10.1007/s00521-024-10197-z36:29(17829-17849)Online publication date: 6-Aug-2024
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