AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017

Computing in Cardiology

Volumn 44, Issue , 2017, Pages 1-4

  Clifford, Gari D ^a,b   Liu, Chengyu ^a,c   Moody, Benjamin ^d   Lehman, Liwei H ^d   Silva, Ikaro ^d   Li, Qiao ^a   Johnson, A E ^a   Mark, Roger G ^d  

^a EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c SOUTHEAST UNIVERSITY   (China)

^d Massachusetts Institute of Technology Cambridge   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; DEEP LEARNING; ELECTROCARDIOGRAPHY; REGRESSION ANALYSIS;

BOOTSTRAP APPROACH; ECG RECORDING; LEARNING APPROACH; NOVEL METHODS; RANDOM FORESTS; SPECTRAL DOMAINS; TRAINING SETS; VOTING APPROACH;

CARDIOLOGY;

EID: 85045094669     PISSN: 23258861     EISSN: 2325887X     Source Type: Conference Proceeding    
DOI: 10.22489/CinC.2017.065-469     Document Type: Conference Paper

References (4)

1. Lip GYH, Fauchier L, Freedman SB, Van Gelder I, Natale A, Gianni C, Nattel S, Potpara T, Rienstra M, Tse H, Lane DA. Atrial fibrillation. Nature Reviews Disease Primers 2016; 2:16016.
2. Camm AJ, et al. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J 2010; 31(19):2369-2429.
3. Zhu T, Johnson AEW, Behar J, Clifford GD. Bayesian voting of multiple annotators for improved QT interval estimation. In Computing in Cardiology, Volume 40. Zaragoza, Spain, Sep 2013; 249-252.
4. Zhu T, Johnson AEW, Behar J, Clifford GD. Crowd-Sourced Annotation of ECG Signals Using Contextual Information. Annals of Biomedical Engineering 2014; 42(4):871-884.