COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS

COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS

Bibliographic Details
Main Author: Jamasebi, Reza
Language:English
Published: Case Western Reserve University School of Graduate Studies / OhioLINK 2008
Subjects:
Entropy
Arousal
SLEEP
Conditional Entropy
TIME SERIES
HTN
AHI
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=case1220467153
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-case12204671532021-08-03T05:32:55Z COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS Jamasebi, Reza Entropy Arousal SLEEP Conditional Entropy TIME SERIES HTN AHI The focus of this dissertation is about contributing to medical research primarily in the areas of collection, processing, analysis, modeling and interpretation of physiological and biological data. The inspiration has been to understand and interrelate different biological and physiological phenomena through a study of observed data by the means of different nonlinear and stochastic signal processing techniques. I particularly study the process of Sleep as a very important physiological phenomenon with the objective of uncovering its relationship with other aspects of biology and physiology. I develop number of novel time series measures that can be used to quantify temporal patterns of this process. I also proposed that these measures can be used as biomarkers for both SDB (Sleep Disorder Breathing) as well as related metabolic disorders. Traditional indices derived from Polysomnography (PSG) data that are available to characterize disease phenotypes have been used to establish associations between Sleep Disorder Breathing (SDB) and other diseases. Although these indices can quantify the severity of SDB to some extent, they do not capture or quantify the dynamic patterns of the sleep process. Consequently, less attention has been given to the temporal patterns of sleep and their relationship to health, the natural aging process, and disease. In this study, we propose novel time series measures that can quantify the temporal patterns of sleep as computational phenotypes and propose that these measures can be used as biomarkers for both SDB as well as related metabolic disorders. 2008-09-05 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1220467153 http://rave.ohiolink.edu/etdc/view?acc_num=case1220467153 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Entropy
Arousal
SLEEP
Conditional Entropy
TIME SERIES
HTN
AHI
spellingShingle Entropy
Arousal
SLEEP
Conditional Entropy
TIME SERIES
HTN
AHI
Jamasebi, Reza
COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS
author Jamasebi, Reza
author_facet Jamasebi, Reza
author_sort Jamasebi, Reza
title COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS
title_short COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS
title_full COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS
title_fullStr COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS
title_full_unstemmed COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS
title_sort computational phenotype derived from physiological time series: application to sleep data analysis
publisher Case Western Reserve University School of Graduate Studies / OhioLINK
publishDate 2008
url http://rave.ohiolink.edu/etdc/view?acc_num=case1220467153
work_keys_str_mv AT jamasebireza computationalphenotypederivedfromphysiologicaltimeseriesapplicationtosleepdataanalysis
_version_ 1719421563299692544

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