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A Tree-based localized functional principal component analysis for ECG features extraction

Wensheng Guo Co-Author
University of Pennsylvania Perelman School of Medicine
 
Wei Yang Co-Author
University of Pennsylvania
 
Zhuoran Ding First Author
 
Zhuoran Ding Presenting Author
 
Sunday, Aug 4: 4:05 PM - 4:20 PM
2542 
Contributed Papers 
Oregon Convention Center 
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 

Main Sponsor

Biometrics Section