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Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning [article]

Maxime Voisin, Yichen Shen, Alireza Aliamiri, Anand Avati, Awni Hannun, Andrew Ng
2018 arXiv   pre-print
We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions.  ...  Continuous and accurate detection of AF from PPG has the potential to transform consumer wearable devices into clinically useful medical monitoring tools.  ...  APPENDIX Figure 1 : 1 Our trained convolutional neural network correctly detects Atrial Fibrillation (AF) from other rhythms (Non-AF) on this PPG recorded with a wrist-wearable device et al.; Figure  ... 
arXiv:1811.07774v2 fatcat:3tdztno5fbd4lhl2vzperitt7e

Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Yu-Chiang Wang, Xiaobo Xu, Adrija Hajra, Samuel Apple, Amrin Kharawala, Gustavo Duarte, Wasla Liaqat, Yiwen Fu, Weijia Li, Yiyun Chen, Robert T. Faillace
2022 Diagnostics  
The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk.  ...  In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation.  ...  Another study sought to validate deep neural network to detect AF using Apple Watch data with two cohorts: patients undergoing electrical or pharmacologic cardioversion and ambulatory patients with self-reported  ... 
doi:10.3390/diagnostics12030689 pmid:35328243 pmcid:PMC8947563 fatcat:zptxj2mcpzbh7bo466tla4rrsq

Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch

Geoffrey H. Tison, José M. Sanchez, Brandon Ballinger, Avesh Singh, Jeffrey E. Olgin, Mark J. Pletcher, Eric Vittinghoff, Emily S. Lee, Shannon M. Fan, Rachel A. Gladstone, Carlos Mikell, Nimit Sohoni (+2 others)
2018 JAMA cardiology  
IMPORTANCE Atrial fibrillation (AF) affects 34 million people worldwide and is a leading cause of stroke.  ...  CONCLUSIONS AND RELEVANCE This proof-of-concept study found that smartwatch photoplethysmography coupled with a deep neural network can passively detect AF but with some loss of sensitivity and specificity  ...  Research Original Investigation Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch the deep neural network automatically learned features representative of a heuristic relevant  ... 
doi:10.1001/jamacardio.2018.0136 pmid:29562087 pmcid:PMC5875390 fatcat:mhaqy7hr2neirj4dgq6ircrk6i

DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices [article]

Jessica Torres Soto, Euan Ashley
2020 arXiv   pre-print
Here, we develop a multi-task deep learning method to assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation (AF).  ...  Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability  ...  Acknowledgements We thank our academic and technology partners who helped with the project, with special thanks to Jeff Christle and Samsung partners at Samsung Strategy and Innovation Centre.  ... 
arXiv:2001.00155v2 fatcat:72t3hchzunbt5jvzemgaw6sfe4

A Ring-type Wearable Device Using Deep Learning Analysis of Photoplethysmographic Signals for Detecting Atrial Fibrillation: A Proof-of-Concept Study (Preprint)

Soonil Kwon, Joonki Hong, Eue-Keun Choi, Byunghwan Lee, Changhyun Baik, Euijae Lee, Eui-Rim Jeong, Bon-Kwon Koo, Seil Oh, Yung Yi
2019 Journal of Medical Internet Research  
Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF).  ...  We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals.  ...  Acknowledgments We would like to thank Dajeong Heo, who helped in the photoplethysmography measurement processes during the study.  ... 
doi:10.2196/16443 pmid:32348254 fatcat:x25fsm7v4bah3ezhooj3i4ls64

NICE atrial fibrillation guideline snubs wearable technology: a missed opportunity?

Andre Briosa e Gala, Michael TB Pope, Milena Leo, Trudie Lobban, Timothy R Betts
2022 Clinical medicine (London)  
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a growing public health epidemic.  ...  Otherwise, there is a real risk of delaying AF diagnosis with the potential of devastating consequences for patients and their families.  ...  Devices used for atrial fibrillation screening. AF = atrial fibrillation; PPG = photoplethysmography; RCTs = randomised controlled trials.  ... 
doi:10.7861/clinmed.2021-0436 pmid:35078798 pmcid:PMC8813025 fatcat:rk2k3k2l3narlmsbk5yshvy6p4

Photoplethysmography based atrial fibrillation detection: a review

Tania Pereira, Nate Tran, Kais Gadhoumi, Michele M. Pelter, Duc H. Do, Randall J. Lee, Rene Colorado, Karl Meisel, Xiao Hu
2020 npj Digital Medicine  
Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality.  ...  Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables.  ...  ., R.C. and K.M. contributed with clinical insights. All authors reviewed the manuscript.  ... 
doi:10.1038/s41746-019-0207-9 pmid:31934647 pmcid:PMC6954115 fatcat:zaqjc27navgsdpfgqj5b3cyk6i

Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification

César A. Millán, Nathalia A. Girón, Diego M. Lopez
2020 International Journal of Environmental Research and Public Health  
Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world's population.  ...  Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection.  ...  Time Domain 6, 7, 8, 14, 15, 24, 28 98.1% 88.7% 95.9% Frequency Domain 30, 32, 36, 37, 39, 41 A Deep Learning Approach to Monitoring and Detecting Atrial Fibrillation using Wearable Technology  ... 
doi:10.3390/ijerph17020498 pmid:31941071 pmcid:PMC7013739 fatcat:2x3bfkzvrrctpjdcem2kvzcg54

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

Solam Lee, Yuseong Chu, Jiseung Ryu, Young Jun Park, Sejung Yang, Sang Baek Koh
2022 Yonsei medical journal  
For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity.  ...  Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed  ...  There have been several studies that used raw ECG with deep learning algorithms, but machine learning models using hand-mance when applied to ambulatory ECG data. 64 In addition, a model that could detect  ... 
doi:10.3349/ymj.2022.63.s93 pmid:35040610 pmcid:PMC8790582 fatcat:4435bs5hxfgchceanecqlp5bvi

Multi-task deep learning for cardiac rhythm detection in wearable devices

Jessica Torres-Soto, Euan A. Ashley
2020 npj Digital Medicine  
Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation  ...  We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of  ...  ACKNOWLEDGEMENTS We thank our academic and technology partners who helped with the project, with thanks to Jeff Christle and Samsung partners at Samsung Strategy and Innovation Centre.  ... 
doi:10.1038/s41746-020-00320-4 pmid:32964139 pmcid:PMC7481177 fatcat:mrccpat6prbqpbp4vrlsgmqogm

Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection

Eemu-Samuli Väliaho, Pekka Kuoppa, Jukka A. Lipponen, Juha E. K. Hartikainen, Helena Jäntti, Tuomas T. Rissanen, Indrek Kolk, Hanna Pohjantähti-Maaroos, Maaret Castrén, Jari Halonen, Mika P. Tarvainen, Onni E. Santala (+1 others)
2021 Frontiers in Physiology  
Photoplethysmography wrist bands accompanied with atrial fibrillation detection algorithms utilizing autocorrelation could provide a computationally very effective and reliable wearable monitoring method  ...  This study evaluated ten robust photoplethysmography features for detection of atrial fibrillation.  ...  For detection of paroxysmal atrial fibrillation, the technology should allow longer rhythm monitoring in ambulatory patients.  ... 
doi:10.3389/fphys.2021.654555 pmid:34025448 pmcid:PMC8138449 fatcat:24fp7xrynvg35ose2q27lkzlwe

Recognising Cardiac Abnormalities in Wearable Device Photoplethysmography (PPG) with Deep Learning [article]

Stewart Whiting, Samuel Moreland, Jason Costello, Glen Colopy, Christopher McCann
2018 arXiv   pre-print
In contrast, current-generation wearables with optical photoplethysmography (PPG) have gained popularity with their low-cost, lack of wires and tiny size.  ...  ECG monitors are typically used to detect these events in electrical heart activity, however they are impractical for continuous long-term use.  ...  For example, [8] used deep learning to identify cardiac events in ECG.  ... 
arXiv:1807.04077v1 fatcat:hdquo2rwxnacrmo7cftgeomrde

Path to precision: prevention of post-operative atrial fibrillation

Rinku Skaria, Saman Parvaneh, Sophia Zhou, James Kim, Santana Wanjiru, Genoveffa Devers, John Konhilas, Zain Khalpey
2020 Journal of Thoracic Disease  
Development of post-operative atrial fibrillation (POAF) following open-heart surgery is a significant clinical and economic burden.  ...  With the advent of more complex machine learning (ML) algorithms and high-throughput sequencing, we have an unprecedented ability to capture and predict POAF in real-time.  ...  Wearable devices Comparable undertakings have been employed to translate and compare electrical signals from ECG and Holter with photoplethysmography (PPG) from wearable devices.  ... 
doi:10.21037/jtd-19-3875 pmid:32642182 pmcid:PMC7330352 fatcat:wezgywsk7bfmngdlur5cjtzp7q

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Chayakrit Krittanawong, Albert J. Rogers, Kipp W. Johnson, Zhen Wang, Mintu P. Turakhia, Jonathan L. Halperin, Sanjiv M. Narayan
2020 Nature Reviews Cardiology  
Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable  ...  Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses.  ...  , hypotensive events or stroke in high-risk patients with atrial fibrillation (AF).  ... 
doi:10.1038/s41569-020-00445-9 pmid:33037325 pmcid:PMC7545156 fatcat:hc2nhy7btvdpncy7ycjhfqesfq

Wearable devices for cardiac arrhythmia detection: a new contender?

Jithin K. Sajeev, Anoop N. Koshy, Andrew W. Teh
2019 Internal medicine journal (Print)  
This coupled with gaps in knowledge pertaining to the optimal management of conditions such as sub clinical atrial fibrillation, may result in unnecessary and expensive downstream testing.  ...  There has been increased consumer uptake of smart devices and wearable technology.  ...  Innovations in big data analysis with machine learning has culminated in development of deep neural networks to identify patients with AF based on PPG guided R-R variability alone 12 .  ... 
doi:10.1111/imj.14274 pmid:31083804 fatcat:xky2goxf2rc4dm2hyfe5oo7eae
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