Print  Close

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

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:

Zhuoran Ding  
N/A

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

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

No

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand