JSM 2005 Online Program

Abstract #303562

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	99
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Biometrics Section
Abstract - #303562
Title:	The Stochastic Modeling of the Sleep-Wakefulness Process with the Development of a Sleep Index for Clinical Applications
Author(s):	Marilisa Gibellato*+ and Haikady Nagaraja
Companies:	The Ohio State University and The Ohio State University
Address:	2523 Dahlia Way, Columbus, OH, 43235, United States
Keywords:	Generalized Gamma Distribution ; Modeling ; Markov Chain
Abstract:	Interest in modeling the sleep process has focused lately on the binary sleep-wakefulness process. Using a dataset consisting of EEG data from 29 subjects over seven days of temporal isolation, we take a parametric approach to describe the overall sleep-wake process architecture. In earlier work, we found the sleep duration times could be modeled as a random sample from a generalized gamma distribution (GGD). We now consider the wake times from each individual. Nonparametric tests reveal a first-order dependence. Further investigation following discretizing the data into four categories finds each sample to represent realizations from a first-order Markov chain. Within the cell containing the longest wake durations, the observations from all subjects are combined to provide an excellent GGD fit. The overall sleep-wake process is next considered, and the successive sleep and wake times found to be independent. This description of the sleep-wake process architecture is then used to suggest a simulation strategy and to develop a sleep index that represents a statistically well-defined measure that can distinguish between our two study groups of differing ages.

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