IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0227401.html
   My bibliography  Save this article

Machine learning detection of Atrial Fibrillation using wearable technology

Author

Listed:
  • Mark Lown
  • Michael Brown
  • Chloë Brown
  • Arthur M Yue
  • Benoy N Shah
  • Simon J Corbett
  • George Lewith
  • Beth Stuart
  • Michael Moore
  • Paul Little

Abstract

Background: Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stroke by approximately two-thirds and can provide a 10% reduction in overall mortality. There has been increased interest in detecting AF due to its increased incidence and the possibility to prevent AF-related strokes. Inexpensive consumer devices which measure the ECG may have the potential to accurately detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF. Methods: We designed a novel AF detection algorithm using a de-correlated Lorenz plot of 60 consecutive RR intervals. In order to reduce the volume of data, the resulting images were compressed using a wavelet transformation (JPEG200 algorithm) and the compressed images were used as input data to a Support Vector Machine (SVM) classifier. We used the Massachusetts Institute of Technology (MIT)—Beth Israel Hospital (BIH) Atrial Fibrillation database and the MIT-BIH Arrhythmia database as training data and verified the algorithm performance using RR intervals collected using an inexpensive consumer heart rate monitor device (Polar-H7) in a case-control study. Results: The SVM algorithm yielded excellent discrimination in the training data with a sensitivity of 99.2% and a specificity of 99.5% for AF. In the validation data, the SVM algorithm correctly identified AF in 79/79 cases; sensitivity 100% (95% CI 95.4%-100%) and non-AF in 328/336 cases; specificity 97.6% (95% CI 95.4%-99.0%). Conclusions: An inexpensive wearable heart rate monitor and machine learning algorithm can be used to detect AF with very high accuracy and has the capability to transmit ECG data which could be used to confirm AF. It could potentially be used for intermittent screening or continuously for prolonged periods to detect paroxysmal AF. Further work could lead to cost-effective and accurate estimation of AF burden and improved risk stratification in AF.

Suggested Citation

  • Mark Lown & Michael Brown & Chloë Brown & Arthur M Yue & Benoy N Shah & Simon J Corbett & George Lewith & Beth Stuart & Michael Moore & Paul Little, 2020. "Machine learning detection of Atrial Fibrillation using wearable technology," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-9, January.
  • Handle: RePEc:plo:pone00:0227401
    DOI: 10.1371/journal.pone.0227401
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227401
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0227401&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0227401?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xiaolin Zhou & Hongxia Ding & Wanqing Wu & Yuanting Zhang, 2015. "A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-16, September.
    2. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark G E White & Neil E Bezodis & Jonathon Neville & Huw Summers & Paul Rees, 2022. "Determining jumping performance from a single body-worn accelerometer using machine learning," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
    2. Richard A. Johansen & Molly K. Reif & Christina L. Saltus & Kaytee L. Pokrzywinski, 2024. "A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a," Sustainability, MDPI, vol. 16(5), pages 1-17, March.
    3. Airola, Antti & Pahikkala, Tapio & Waegeman, Willem & De Baets, Bernard & Salakoski, Tapio, 2011. "An experimental comparison of cross-validation techniques for estimating the area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1828-1844, April.
    4. Matthias Schmid & Thomas Hielscher & Thomas Augustin & Olaf Gefeller, 2011. "A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error," Biometrics, The International Biometric Society, vol. 67(2), pages 524-535, June.
    5. Luts, Jan & Ormerod, John T., 2014. "Mean field variational Bayesian inference for support vector machine classification," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 163-176.
    6. David Rios Insua & Roi Naveiro & Victor Gallego, 2020. "Perspectives on Adversarial Classification," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
    7. John J Nay & Yevgeniy Vorobeychik, 2016. "Predicting Human Cooperation," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    8. Matthew Tuson & Berwin Turlach & Kevin Murray & Mei Ruu Kok & Alistair Vickery & David Whyatt, 2021. "Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation," IJERPH, MDPI, vol. 18(19), pages 1-21, September.
    9. Lauri Nevasalmi, 2022. "Recession forecasting with high‐dimensional data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 752-764, July.
    10. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
    11. Hosseini, Fatemeh & Eidsvik, Jo & Mohammadzadeh, Mohsen, 2011. "Approximate Bayesian inference in spatial GLMM with skew normal latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1791-1806, April.
    12. Gonzalo Perez-de-la-Cruz & Guillermina Eslava-Gomez, 2019. "Discriminant analysis for discrete variables derived from a tree-structured graphical model," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 855-876, December.
    13. I. Charvet & A. Suppasri & H. Kimura & D. Sugawara & F. Imamura, 2015. "A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 2073-2099, December.
    14. Shusaku Tsumoto & Tomohirno Kimura & Shoji Hirano, 2021. "Determination of Disease from Discharge Summaries," The Review of Socionetwork Strategies, Springer, vol. 15(1), pages 49-66, June.
    15. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2010. "Fast robust estimation of prediction error based on resampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3121-3130, December.
    16. Chen, Weijie & Yousef, Waleed A. & Gallas, Brandon D. & Hsu, Elizabeth R. & Lababidi, Samir & Tang, Rong & Pennello, Gene A. & Symmans, W. Fraser & Pusztai, Lajos, 2012. "Uncertainty estimation with a finite dataset in the assessment of classification models," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1016-1027.
    17. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.
    18. Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.
    19. Charlotte Soneson & Sarah Gerster & Mauro Delorenzi, 2014. "Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
    20. Zhengnan Huang & Hongjiu Zhang & Jonathan Boss & Stephen A Goutman & Bhramar Mukherjee & Ivo D Dinov & Yuanfang Guan & for the Pooled Resource Open-Access ALS Clinical Trials Consortium, 2017. "Complete hazard ranking to analyze right-censored data: An ALS survival study," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-21, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0227401. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.