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Nonparametric spectral analysis of heart rate variability through penalized sum of squares
Author(s) -
Krafty Robert T.,
Zhao Mengyuan,
Buysse Daniel J.,
Thayer Julian F.,
Hall Martica
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6038
Subject(s) - nonparametric statistics , heart rate variability , smoothing , statistics , computer science , series (stratigraphy) , time series , interval (graph theory) , mathematics , algorithm , pattern recognition (psychology) , econometrics , artificial intelligence , heart rate , medicine , paleontology , blood pressure , biology , combinatorics
Researchers in a variety of biomedical fields have utilized frequency domain properties of heart rate variability (HRV), or the elapsed time between consecutive heart beats. HRV is measured from the electrocardiograph signal through the interbeat interval series. Popular approaches for estimating power spectra from these interval data apply common spectral analysis methods that are designed for the analysis of evenly sampled time series. The application of these methods to the interbeat interval series, which is indexed over an uneven time grid, requires a bias‐inducing transformation. The goal of this article is to explore the use of penalized sum of squares for nonparametric estimation of the spectrum of HRV directly from the interbeat intervals. A novel cross‐validation procedure is introduced for smoothing parameter selection. Empirical properties of the proposed estimation procedure are explored and compared with popular methods in a simulation study. The proposed method is used in an analysis of data from an insomnia study, which seeks to illuminate the association between the power spectrum of HRV during different periods of sleep with response to behavioral therapy. Copyright © 2013 John Wiley & Sons, Ltd.