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dc.contributor.authorMoody, Benjamin Edward
dc.contributor.authorLehman, Li-Wei
dc.contributor.authorSilva, Ikaro
dc.contributor.authorJohnson, A.
dc.contributor.authorMark, Roger G
dc.date.accessioned2020-08-13T17:03:28Z
dc.date.available2020-08-13T17:03:28Z
dc.date.issued2017-09
dc.identifier.issn0276-6574
dc.identifier.issn2325-887X
dc.identifier.urihttps://hdl.handle.net/1721.1/126563
dc.description.abstractThe PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings performed by patients. A total of 12, 186 ECGs were used: 8, 528 in the public training set and 3, 658 in the private hidden test set. Due to the high degree of interexpert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants' algorithms to identify contentious labels. A total of 75 independent teams entered the Challenge using a variety of traditional and novel methods, ranging from random forests to a deep learning approach applied to the raw data in the spectral domain. Four teams won the Challenge with an equal high F1 score (averaged across all classes) of 0.83, although the top 11 algorithms scored within 2% of this. A combination of 45 algorithms identified using LASSO achieved an F1 of 0.87, indicating that a voting approach can boost performance.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant R01-GM104987)en_US
dc.language.isoen
dc.publisherComputing in Cardiologyen_US
dc.relation.isversionof10.22489/CINC.2017.065-469en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleAF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017en_US
dc.typeArticleen_US
dc.identifier.citationClifford, Gari D. et al. “AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017.” Computing in cardiology, vol. 44, 2017 © 2017 The Author(s)en_US
dc.contributor.departmentInstitute for Medical Engineering and Scienceen_US
dc.relation.journalComputing in cardiologyen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-10-09T15:36:25Z
dspace.date.submission2019-10-09T15:36:27Z
mit.journal.volume44en_US


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