Abstract Number:
2542
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Zhuoran Ding (1), Wensheng Guo (1), Wei Yang (1)
Institutions:
(1) University of Pennsylvania Perelman School of Medicine, N/A
Co-Author(s):
Wensheng Guo
University of Pennsylvania Perelman School of Medicine
Wei Yang
University of Pennsylvania Perelman School of Medicine
First Author:
Zhuoran Ding
University of Pennsylvania Perelman School of Medicine
Presenting Author:
Abstract Text:
In this study, we propose a novel tree-based localized functional principal component analysis method. Eigenfunctions estimated by the proposed method have compact local supports and can be interpreted as local features. We demonstrate the proposed method with an application to the electrocardiogram (ECG) data collected from the Chronic Renal Insufficiency Cohort (CRIC) study. The proposed method identified that delayed and decreased P wave, decreased amplitude of the Q and R wave and abnormal S wave, delayed onset of the T wave, and decreased T wave are associated with atrial fibrillation (AFib). A multivariable predictive model for AFib status using these local features is constructed with a C statistic of 0.771.
Keywords:
functional data analysis|functional principal component analysis|electrocardiogram|tree-based method| |
Sponsors:
Biometrics Section
Tracks:
Semiparametric Modeling
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