Smooth Tensor Decomposition for Ambulatory Blood Pressure Monitoring Data

Music City Center 

Ambulatory blood pressure monitoring (ABPM) is widely used to track blood pressure and heart rate over periods of 24 hours or more. Most existing studies rely on basic summary statistics of ABPM data, such as means or medians, which obscure temporal features like nocturnal dipping and individual chronotypes. To better characterize the temporal features of ABPM data, we propose a novel smooth tensor decomposition method. Built upon traditional low-rank tensor factorization techniques, our method incorporates a smoothing penalty to handle noise and employs an iterative algorithm to impute missing data. We also develop an automatic approach for the selection of optimal smoothing parameters and ranks. We apply our method to ABPM data from patients with concurrent obstructive sleep apnea and type II diabetes. Our method explains temporal components of data variation and outperforms the traditional approach of using summary statistics in capturing the associations between covariates and ABPM measurements. Notably, it distinguishes covariates that influence the overall levels of blood pressure and heart rate from those that affect the contrast between the two.

Low-rank tensor factorization

Smoothing penalty

Missing data imputation 

Section on Statistical Learning and Data Science