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Machine learning detection of atrial fibrillation using wearable technology

Machine learning detection of atrial fibrillation using wearable technology
Machine learning detection of atrial fibrillation using wearable technology
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.
Atrial fibrillation, Machine Learning, Medical Technology
1932-6203
Lown, Mark
4742d5f8-bcf3-4e0b-811c-920e7d010c9b
Brown, Michael
a653384d-4fa7-4987-b03c-8a6ce153c6e7
Brown, Chloe
4193c6eb-fccb-49f1-a4bd-9837edbd28c4
Yue, Arthur M.
4040f8ec-0252-49e0-b838-58f37956acd2
Shah, Benoy
0f73f809-dfbf-48ed-a624-b95b6c2aa431
Corbett, Simon
325a1edd-5325-4981-a5df-787d53f36d5e
Lewith, George
0fc483fa-f17b-47c5-94d9-5c15e65a7625
Stuart, Beth
626862fc-892b-4f6d-9cbb-7a8d7172b209
Moore, Michael
1be81dad-7120-45f0-bbed-f3b0cc0cfe99
Little, Paul
1bf2d1f7-200c-47a5-ab16-fe5a8756a777
Lown, Mark
4742d5f8-bcf3-4e0b-811c-920e7d010c9b
Brown, Michael
a653384d-4fa7-4987-b03c-8a6ce153c6e7
Brown, Chloe
4193c6eb-fccb-49f1-a4bd-9837edbd28c4
Yue, Arthur M.
4040f8ec-0252-49e0-b838-58f37956acd2
Shah, Benoy
0f73f809-dfbf-48ed-a624-b95b6c2aa431
Corbett, Simon
325a1edd-5325-4981-a5df-787d53f36d5e
Lewith, George
0fc483fa-f17b-47c5-94d9-5c15e65a7625
Stuart, Beth
626862fc-892b-4f6d-9cbb-7a8d7172b209
Moore, Michael
1be81dad-7120-45f0-bbed-f3b0cc0cfe99
Little, Paul
1bf2d1f7-200c-47a5-ab16-fe5a8756a777

Lown, Mark, Brown, Michael, Brown, Chloe, Yue, Arthur M., Shah, Benoy, Corbett, Simon, Lewith, George, Stuart, Beth, Moore, Michael and Little, Paul (2020) Machine learning detection of atrial fibrillation using wearable technology. PLoS ONE, 15 (1), [e0227401]. (doi:10.1371/journal.pone.0227401).

Record type: Article

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.

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More information

Accepted/In Press date: 4 December 2019
Published date: 24 January 2020
Keywords: Atrial fibrillation, Machine Learning, Medical Technology

Identifiers

Local EPrints ID: 438385
URI: http://eprints.soton.ac.uk/id/eprint/438385
ISSN: 1932-6203
PURE UUID: dbb32a9b-f803-4c50-a29f-5167d158a385
ORCID for Mark Lown: ORCID iD orcid.org/0000-0001-8309-568X
ORCID for Beth Stuart: ORCID iD orcid.org/0000-0001-5432-7437
ORCID for Michael Moore: ORCID iD orcid.org/0000-0002-5127-4509

Catalogue record

Date deposited: 09 Mar 2020 17:30
Last modified: 10 Jan 2022 03:04

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Contributors

Author: Mark Lown ORCID iD
Author: Michael Brown
Author: Chloe Brown
Author: Arthur M. Yue
Author: Benoy Shah
Author: Simon Corbett
Author: George Lewith
Author: Beth Stuart ORCID iD
Author: Michael Moore ORCID iD
Author: Paul Little

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