Machine learning detection of Atrial Fibrillation using wearable technology
Autoři:
Mark Lown aff001; Michael Brown aff002; Chloë Brown aff001; Arthur M. Yue aff003; Benoy N. Shah aff003; Simon J. Corbett aff003; George Lewith aff003; Beth Stuart aff001; Michael Moore aff001; Paul Little aff001
Působiště autorů:
Primary Care & Population Sciences, Faculty of Medicine, University of Southampton, Southampton, England
aff001; Leonardo MW Ltd, Southampton, England
aff002; Cardiology and Electrophysiology, Southampton General Hospital, Southampton, England
aff003
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227401
Souhrn
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.
Klíčová slova:
Algorithms – Arrhythmia – Atrial fibrillation – Electrocardiography – Heart rate – Machine learning algorithms – Measurement equipment – Support vector machines
Zdroje
1. Patel NJ, Atti V, Mitrani RD, Viles-Gonzalez JF, Goldberger JJ. Global rising trends of atrial fibrillation: a major public health concern. Heart. 2018 Dec;104(24):1989–1990. doi: 10.1136/heartjnl-2018-313350 29907645
2. Reiffel JA, Atrial fibrillation and stroke: epidemiology. Am J Med. 2014 Apr;127(4):e15–6.
3. Lown M, Moran P. Should we screen for atrial fibrillation? BMJ. 2019 Feb 13;364:l43. doi: 10.1136/bmj.l43 30760476
4. Granger CB, Armaganijan LV. Newer oral anticoagulants should be used as first-line agents to prevent thromboembolism in patients with atrial fibrillation and risk factors for stroke or thromboembolism. Circulation. 2012 Jan 3;125(1):159–64; doi: 10.1161/CIRCULATIONAHA.111.031146 22215890
5. Turakhia MP, Desai M, Hedlin H, Rajmane A, Talati N, Ferris T et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am Heart J. 2019 Jan;207:66–75. doi: 10.1016/j.ahj.2018.09.002 30392584
6. Bonomi AG, Schipper F, Eerikäinen LM, Margarito J, van Dinther R, Muesch G, et al. Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo-Plethysmography at the Wrist. J Am Heart Assoc. 2018 Aug 7;7(15):
7. https://www.suunto.com/en-gb/Content-pages/what-should-you-know-about-wrist-heart-rate2/. Accessed 01/03/2019
8. Koshy AN, Sajeev JK, Nerlekar N, Brown AJ, Rajakariar K, Zureik M, et al. Smart watches for heart rate assessment in atrial arrhythmias. Int J Cardiol. 2018 Sep 1;266:124–127. doi: 10.1016/j.ijcard.2018.02.073 29887428
9. Freedman B, Camm J, Calkins H, Healey JS, Rosenqvist M, Wang J, et al.; AF-Screen Collaborators. Screening for Atrial Fibrillation: A Report of the AF-SCREEN International Collaboration. Circulation. 2017 May 9;135(19):1851–1867. doi: 10.1161/CIRCULATIONAHA.116.026693 28483832
10. Hartikainen S, Lipponen JA, Hiltunen P, Rissanen TT, Kolk I, Tarvainen MP, et al. Effectiveness of the Chest Strap Electrocardiogram to Detect Atrial Fibrillation. Am J Cardiol. 2019 Feb 23.
11. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402–2410. doi: 10.1001/jama.2016.17216 27898976
12. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al.; the CAMELYON16 Consortium. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017 Dec 12;318(22):2199–2210. doi: 10.1001/jama.2017.14585 29234806
13. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 2000;101:e215–e220. doi: 10.1161/01.cir.101.23.e215 10851218
14. Deshmukh A, Brown ML, Higgins E, Schousek B, Abeyratne A, Rovaris G, et al. Performance of Atrial Fibrillation Detection in a New Single-Chamber ICD. Pacing Clin Electrophysiol. 2016;39:1031–1037. 27433785
15. Zeng W, Daly S. An overview of the visual optimization tools in JPEG 2000. Signal Process: Image Comm. 2002;17:85–104.
16. Ji-Hyun Kim. 2009. Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics & Data Analysis, 53(11):3735–3745.
17. Lown M, Yue A, Lewith G, Little P, Moore M. Screening for Atrial Fibrillation using Economical and accurate TechnologY (SAFETY)-a pilot study. BMJ Open. 2017;13;7 1:e013535. doi: 10.1136/bmjopen-2016-013535 28087552
18. Giles D, Draper N, Neil W. Validity of the Polar V800 heart rate monitor to measure RR intervals at rest. Eur J Appl Physiol 2016;116:563–71. doi: 10.1007/s00421-015-3303-9 26708360
19. Lown M, Yue AM, Shah BN, Corbett SJ, Lewith G, Stuart B, et al. Screening for Atrial Fibrillation Using Economical and Accurate Technology (From the SAFETY Study). Am J Cardiol. 2018 Oct 15;122(8):1339–1344. doi: 10.1016/j.amjcard.2018.07.003 30131106
20. Zhou X, Ding H, Wu W, Zhang Y. A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate. PLoS One. 2015 Sep 16;10(9):e0136544. doi: 10.1371/journal.pone.0136544 26376341
21. Andersen RS, Poulsen ES, Puthusserypady S. A novel approach for automatic detection of Atrial Fibrillation based on Inter Beat Intervals and Support Vector Machine. Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:2039–2042. doi: 10.1109/EMBC.2017.8037253 29060297
22. Gilani M, Eklund JM, Makrehchi M. Automated detection of atrial fibrillation episode using novel heart rate variability features. Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:3461–3464. doi: 10.1109/EMBC.2016.7591473 28269045
23. Desteghe L, Raymaekers Z, Lutin M, Vijgen J, Dilling-Boer D, Koopman P, et al. Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting. Europace. 2017 Jan;19(1):29–39. doi: 10.1093/europace/euw025 26893496
24. Lowres N, Neubeck L, Salkeld G, Krass I, McLachlan AJ, Redfern J, et al. Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. Thromb Haemost. 2014;111:1167–1176. doi: 10.1160/TH14-03-0231 24687081
25. Kearley K, Selwood M, Van den Bruel A, Thompson M, Mant D, Hobbs FR, et al. Triage tests for identifying atrial fibrillation in primary care: a diagnostic accuracy study comparing single-lead ECG and modified BP monitors. BMJ Open. 2014;45:e004565.
26. Chen LY, Chung MK, Allen LA, Ezekowitz M, Furie KL, McCabe P, et al.; American Heart Association Council on Clinical Cardiology; Council on Cardiovascular and Stroke Nursing; Council on Quality of Care and Outcomes Research; and Stroke Council. Atrial Fibrillation Burden: Moving Beyond Atrial Fibrillation as a Binary Entity: A Scientific Statement From the American Heart Association. Circulation. 2018 May 15;137(20):e623–e644. doi: 10.1161/CIR.0000000000000568 29661944
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