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Multiscale multivariate functional principal component analysis with an application to multivariate longitudinal cardiac signals

Potter, Andrew (2017) Multiscale multivariate functional principal component analysis with an application to multivariate longitudinal cardiac signals. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Circadian cycles in humans are an important health indicator in cardiovascular disease. With recent developments in ventricular assist devices (VADs), continuous recording of cardiac circadian cycles in cohorts of heart failure patients is now possible for the entire life of the implant. Specifically, VADs continuously record multivariate data on blood flow and device status providing a unique longitudinal view of circadian cycles in these cohorts.

Our statistical challenge is to simultaneously model the cohort average pump output (PO) and pulsatility (PI) circadian cycle measurements and patient specific longitudinal evolution of his/her circadian cycle. While functional principal components analysis (FPCA) methods exist for the analysis of univariate longitudinal functional data with this structure, these techniques do not address bivariate functional data.

We first divide time into two time scales: "fast" (circadian) and "slow" (longitudinal). We assume that the data are generated by smooth functions of time and extend FPCA to include both time scales. Use of a marginal model separates the estimation and inference for the two time scales. On the circadian time scale, we use wavelet based FPCA to estimate the cohort mean cycle and subject specific cycles. Confidence bands for the cohort mean and other estimates are calculated with a bootstrap. On the longitudinal time scale, a second FPCA step captures the subject specific longitudinal evolution. Furthermore, using data from VAD patients, we implement our method to characterize the population circadian cycle and identify regions of high between-subject variability in both the fast and slow time scales.

Our model provides a novel approach for analyzing multivariate circadian cycles. This work opens new avenues to understand the relationship between circadian cycles in simultaneously recorded cardiovascular measurements. The public health significance is that care can be improved with better understanding of the longitudinal course of these patients.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Potter, Andrewanp88@pitt.eduanp88
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairAnderson, Stewartsja@pitt.edusja
Committee MemberKrafty, Robertrkrafty@pitt.edurkrafty
Committee MemberDing, Yingyingding@pitt.eduyingding
Committee MemberTeuteberg, Jeffreyjjt19@pitt.edujjt19
Committee MemberChen, Kehuikhchen@pitt.edukhchen
Date: 24 February 2017
Date Type: Publication
Defense Date: 2 December 2016
Approval Date: 24 February 2017
Submission Date: 22 November 2016
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 115
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Functional Data Analysis, Discrete Wavelet Transformation, Marginal Covariance Kernel, Physiological Signal Analysis, Circadian Cycle, Chronobiology
Date Deposited: 24 Feb 2017 19:16
Last Modified: 01 Jan 2019 06:15


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