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Detection of Atrial Fibrillation Using Decision Tree Ensemble
conference contribution
posted on 2023-08-30, 15:33 authored by Guangyu Bin, Minggang Shao, Guanghong Bin, Jiao Huang, Dingchang Zheng, Shuicai Wu2017 PhysioNet/CinC Challenge proposed a global competition for classifying a short single ECG lead recording into normal sinus rhythm, atrial fibrillation (AF), alternative rhythm, and unclassified rhythm. This study developed and evaluated a pragmatic approach to solve the challenge, which was based on a decision tree ensemble with 30 features from ECG recording. The model was trained using the AdaBoost.M2 algorithm. The results reported here were obtained using 100-fold cross-validation, and the lowest MSE was 0.12 with the maximum number of splits of 55, and the number of trees of 20. The entry was tested and scored in the second phase of the challenge. The achieved scores for "Normal", "AF", "Other", were 0.93, 0.86, and 0.79, respectively, while the F1 measure was 0.86, and the official overall score was 0.82.
History
Page range
1-4ISSN
2325-887XExternal DOI
Publisher
IEEEPlace of publication
OnlineISBN
978-1-5386-6630-2Conference proceeding
2017 Computing in Cardiology (CinC)Name of event
2017 Computing in Cardiology (CinC)Location
Rennes, FranceEvent start date
2017-09-24Event finish date
2017-09-27File version
- Accepted version
Language
- eng
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Legacy posted date
2018-08-16Legacy creation date
2018-08-15Legacy Faculty/School/Department
ARCHIVED Faculty of Medical Science (until September 2018)Note
© 2017 IEEEUsage metrics
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