poster

Respiratory events screening using consumer smartwatches

Authors:

Kostyantyn Slyusarenko,

Margaryta NastenkoAuthors Info & Claims

UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

Pages 25 - 28

https://doi.org/10.1145/3410530.3414399

Published: 12 September 2020 Publication History

Abstract

Respiratory related events (RE) during nocturnal sleep disturb the natural physiological pattern of sleep. This events may include all types of apnea and hypopnea, respiratory-event-related arousals and snoring. The particular importance of breath analysis is currently associated with the COVID-19 pandemic. The proposed algorithm is a deep learning model with long short-term memory cells for RE detection for each 1 minute epoch during nocturnal sleep. Our approach provides the basis for a smartwatch based respiratory-related sleep pattern analysis (accuracy of epoch-by-epoch classification is greater than 80 %), can be applied for a potential risk of respiratory-related diseases screening (mean absolute error of AHI estimation is about 6.5 events/h on the test set, which includes participants with all types of apnea severity; two class screening accuracy (AHI threshold is 15 events/h) is greater than 90 %).

References

[1]

Paul S. Addison. 2017. Respiratory effort from the photoplethysmogram. Medical Engineering & Physics 41 (2017), 9 -- 18.

[2]

Richard Berry, Rita Brooks, Charlene Gamaldo, Susan Harding, Carole Marcus, and Bradley Vaughn. 2015. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications version 2 (2nd. ed.). American Academy of Sleep Medicine, Darien, Illinois.

[3]

Hsiao-Huang Chang, Chuan-Chih Hsu, Chia-Yuen Chen, Wai-Keung Lee, Hao-Teng Hsu, Kuo-Kai Shyu, Jia-Rong Yeh, Pin-Jun Lin, and Po-Lei Lee. 2018. A Method for Respiration Rate Detection in Wrist PPG Signal Using Holo-Hilbert Spectrum. IEEE Sensors Journal 18, 18 (2018), 7560--7569.

[4]

Krittanawong Chayakrit, W. Johnson Kipp, S Rosenson Robert, Wang Zhen, Aydar Mehmet, Baber Usman, K. Min James, Tang W H Wilson, L. Halperin Jonathan, and M. Narayan Sanjiv. 2019. Deep learning for cardiovascular medicine: a practical primer. European heart journal 40, 25 (2019), 2058--2073.

[5]

Massimiliano de Zambotti, Job G. Godino, Fiona C. Baker, Joseph Cheung, Kevin Patrick, and Ian M. Colrain. 2016. The Boom in Wearable Technology: Cause for Alarm or Just What is Needed to Better Understand Sleep? Sleep 39 9 (2016), 1761--2.

[6]

D. Chan Edward, M. Chan Michael, and M. Chan Mallory. 2013. Pulse oximetry: Understanding its basic principles facilitates appreciation of its limitations. Respiratory Medicine 107, 6 (2013), 789--799.

[7]

Illia Fedorin, Kostyantyn Slyusarenko, Wonkyu Lee, and N. Sakhnenko. 2019. Sleep Stages Classification in a Healthy People Based on Optical Plethysmography and Accelerometer Signals via Wearable Devices. In 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON). IEEE, Lviv, Ukraine, 1201--1204.

[8]

Anastasiia Havriushenko, Kostyantyn Slyusarenko, and Illia Fedorin. 2020. Smartwatch based respiratory rate estimation during sleep using CNN/LSTM neural network. In 2020 IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO). IEEE, Kyiv, Ukraine, 584--587.

[9]

Roee Landsberg, Michael Friedman, and Jessica Ascher-Landsberg. 2001. Treatment of Hypoxemia in Obstructive Sleep Apnea. American journal of rhinology 15 (09 2001), 311--313.

[10]

M Melani Lyons, Jan F Kraemer, Radha Dhingra, Brendan T Keenan, Niels Wessel, Martin Glos, Thomas Penzel, and Indira Gurubhagavatula. 2019. Screening for Obstructive Sleep Apnea in Commercial Drivers Using EKG-Derived Respiratory Power Index. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 15, 1 (2019), 23--32.

[11]

Mads Olsen, Emmanuel Mignot, Poul Jorgen Jennum, and Helge Bjarup Dissing Sorensen. 2019. Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts. Sleep 43, 5 (2019), zsz276.

[12]

Gabriele Papini, Pedro Fonseca, Merel Gilst, Jan Bergmans, Rik Vullings, and Sebastiaan Overeem. 2020. Respiratory activity extracted from wrist-worn reflective photoplethysmography in a sleep-disordered population. Physiological Measurement (2020).

[13]

Gabriele Papini, Pedro Fonseca, Merel Gilst, Johannes van dijk, Dirk Pevernagie, Jan Bergmans, Rik Vullings, and Sebastiaan Overeem. 2019. Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features. Scientific Reports 9 (11 2019).

[14]

Mehdi Shokoueinejad, Chris Fernandez, Emily Carroll, Fa Wang, Jake Levin, Sam Rusk, Nick Glattard, Ashley Mulchrone, Xuan Zhang, Ailiang Xie, Mihaela Teodorescu, Jerome Dempsey, and John Webster. 2017. Sleep apnea: a review of diagnostic sensors, algorithms, and therapies. Physiological Measurement 38, 9 (2017), R204--R252.

[15]

Kostyantyn Slyusarenko, Illia Fedorin, and Wonkyu Lee. 2019. Sleep Stages Classifier with Eliminated Apnea Impact. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (London, United Kingdom) (UbiComp/ISWC '19 Adjunct). Association for Computing Machinery, New York, NY, USA, 210--213.

Cited By

Abd-alrazaq AAslam HAlSaad RAlsahli MAhmed ADamseh RAziz SSheikh J(2024)Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-AnalysisJournal of Medical Internet Research10.2196/5818726(e58187)Online publication date: 10-Sep-2024
https://doi.org/10.2196/58187
Fedorin ISmielova ANastenko MKrasnoshchok I(2024)From Sprint to Recovery: LSTM-Powered Heart Rate Recovery Forecasting in HIIT Sessions2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10781668(1-4)Online publication date: 15-Jul-2024
https://doi.org/10.1109/EMBC53108.2024.10781668
Lee PGu WHuang WChiang A(2024)From Screening at Clinic to Diagnosis at Home: How AI/ML/DL Algorithms Are Transforming Sleep Apnea DetectionHandbook of AI and Data Sciences for Sleep Disorders10.1007/978-3-031-68263-6_4(109-160)Online publication date: 19-Oct-2024
https://doi.org/10.1007/978-3-031-68263-6_4
Show More Cited By

Index Terms

Respiratory events screening using consumer smartwatches
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Information

Published In

cover image ACM Conferences

UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

September 2020

732 pages

ISBN:9781450380768

DOI:10.1145/3410530

General Chairs:
Monica Tentori
CICESE, Mexico
,
Nadir Weibel
UC San Diego
,
Kristof Van Laerhoven
University of Siegen, Germany
,
Program Chairs:
Gregory Abowd
Georgia Tech
,
Flora Salim
RMIT, Australia

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 12 September 2020

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UbiComp/ISWC '20

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UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers

September 12 - 17, 2020

Virtual Event, Mexico

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Abd-alrazaq AAslam HAlSaad RAlsahli MAhmed ADamseh RAziz SSheikh J(2024)Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-AnalysisJournal of Medical Internet Research10.2196/5818726(e58187)Online publication date: 10-Sep-2024
https://doi.org/10.2196/58187
Fedorin ISmielova ANastenko MKrasnoshchok I(2024)From Sprint to Recovery: LSTM-Powered Heart Rate Recovery Forecasting in HIIT Sessions2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10781668(1-4)Online publication date: 15-Jul-2024
https://doi.org/10.1109/EMBC53108.2024.10781668
Lee PGu WHuang WChiang A(2024)From Screening at Clinic to Diagnosis at Home: How AI/ML/DL Algorithms Are Transforming Sleep Apnea DetectionHandbook of AI and Data Sciences for Sleep Disorders10.1007/978-3-031-68263-6_4(109-160)Online publication date: 19-Oct-2024
https://doi.org/10.1007/978-3-031-68263-6_4
Fedorin ISmielova ANastenko MKrasnoshchok I(2023)Forecasting of breathing events during nocturnal sleep using encoder-decoder recurrent neural network based on a sensors data of consumer smartwatches2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10339945(1-4)Online publication date: 24-Jul-2023
https://doi.org/10.1109/EMBC40787.2023.10339945
Boulemtafes ADerhab AChallal Y(2022)Privacy-preserving deep learning for pervasive health monitoring: a study of environment requirements and existing solutions adequacyHealth and Technology10.1007/s12553-022-00640-312:2(285-304)Online publication date: 4-Feb-2022
https://doi.org/10.1007/s12553-022-00640-3
Hijazi HAbu Talib MHasasneh ABou Nassif AAhmed NNasir Q(2021)Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19Sensors10.3390/s2124842421:24(8424)Online publication date: 17-Dec-2021
https://doi.org/10.3390/s21248424
Fedorin ISlyusarenko K(2021)Consumer Smartwatches As a Portable PSG: LSTM Based Neural Networks for a Sleep-Related Physiological Parameters Estimation2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC46164.2021.9629597(849-452)Online publication date: 1-Nov-2021
https://doi.org/10.1109/EMBC46164.2021.9629597
Boulemtafes ADerhab AAli Braham NChallal Y(2021)PReDIHERO – Privacy-Preserving Remote Deep Learning Inference based on Homomorphic Encryption and Reversible Obfuscation for Enhanced Client-side Overhead in Pervasive Health Monitoring2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA53542.2021.9686893(1-8)Online publication date: Nov-2021
https://doi.org/10.1109/AICCSA53542.2021.9686893

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