Detection of atrial fibrillation using decision tree ensemble

Guanguo Bin, Minggang Shao, Guanghong Bin, Jiao Huang, Dingchang Zheng, Shuicai Wu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

11 Citations (Scopus)

Abstract

2017 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.
Original languageEnglish
Title of host publicationComputing in Cardiology
PublisherIEEE
ISBN (Electronic) 978-1-5386-6630-2
ISBN (Print)978-1-5386-4555-0
DOIs
Publication statusPublished - 5 Apr 2018
Externally publishedYes
EventComputing in Cardiology 2017 - Rennes, France
Duration: 24 Sep 201727 Sep 2017
Conference number: 44
https://dblp.org/db/conf/cinc/cinc2017

Conference

ConferenceComputing in Cardiology 2017
Abbreviated titleCINC 2017
CountryFrance
CityRennes
Period24/09/1727/09/17
Internet address

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  • Cite this

    Bin, G., Shao, M., Bin, G., Huang, J., Zheng, D., & Wu, S. (2018). Detection of atrial fibrillation using decision tree ensemble. In Computing in Cardiology IEEE. https://doi.org/10.22489/CinC.2017.342-204