Title page for ETD etd-032608-125908

Document Type thesis

Author Name Misra, Shivin Satyawon

Email Address shivinm at gmail.com

URN etd-032608-125908

Title A Database For Exploratory Analysis of Human Sleep

Degree MS

Department Computer Science

Advisors
Carolina Ruiz, Advisor
Sergio Alvarez, Co-Advisor
Elke Rundensteiner, Reader
Michael A. Gennert, Department Head

Keywords
postgres
computer science
database
sleep
terabyte
data mining
weka

Date of Presentation/Defense 2008-03-21

Availability unrestricted

Abstract
This thesis focuses on the design, development, and exploratory analysis of a human sleep data repository. We have successfully collected comprehensive data for 1,046 sleep disorder patients and created a Terabyte-scale database system to handle it. The data for each patient was collected from the patient's medical records, and from the patient's allnight sleep study (for a total of about 0.6 Gigabytes per patient). Data collected from the patient's medical record contain more than 70 attributes, including demographic data, smoking, drinking, and exercise habits, depression and daytime sleepiness questionnaires, and overall medical history. Data collected from the patient's all-night sleep study consist of 50-55 time-series signals recorded during a period of 6-8 hours at the hospital's sleep clinic. These signals include among others an electroencephalogram, electromyogram, electrooculogram, electrocardiogram, and signals tracking blood oxygen level, body position, limb movements, snoring and blood pressure. 350 additional attributes summarize sleep related events taking
place during the night long study, including sleep stages, arousals, and respiratory disturbances.

Particular attention during the development of our database system was paid to a database design that effectively handles the data size and complexity, that describes the structure of sleep data in clinically meaningful terms, and that will facilitates the discovery of patterns in sleep data using machine learning algorithms. We have interfaced our database with Weka, a well known data mining system. To the best of our knowledge, our database is one of the world's largest and most comprehensive in the domain of human sleep disorders.

Files
Shivin_Misra_thesis_2008.pdf

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Document Type	thesis
Author Name	Misra, Shivin Satyawon
Email Address	shivinm at gmail.com
URN	etd-032608-125908
Title	A Database For Exploratory Analysis of Human Sleep
Degree	MS
Department	Computer Science
Advisors	Carolina Ruiz, Advisor Sergio Alvarez, Co-Advisor Elke Rundensteiner, Reader Michael A. Gennert, Department Head
Keywords	postgres computer science database sleep terabyte data mining weka
Date of Presentation/Defense	2008-03-21
Availability	unrestricted
Abstract This thesis focuses on the design, development, and exploratory analysis of a human sleep data repository. We have successfully collected comprehensive data for 1,046 sleep disorder patients and created a Terabyte-scale database system to handle it. The data for each patient was collected from the patient's medical records, and from the patient's allnight sleep study (for a total of about 0.6 Gigabytes per patient). Data collected from the patient's medical record contain more than 70 attributes, including demographic data, smoking, drinking, and exercise habits, depression and daytime sleepiness questionnaires, and overall medical history. Data collected from the patient's all-night sleep study consist of 50-55 time-series signals recorded during a period of 6-8 hours at the hospital's sleep clinic. These signals include among others an electroencephalogram, electromyogram, electrooculogram, electrocardiogram, and signals tracking blood oxygen level, body position, limb movements, snoring and blood pressure. 350 additional attributes summarize sleep related events taking place during the night long study, including sleep stages, arousals, and respiratory disturbances. Particular attention during the development of our database system was paid to a database design that effectively handles the data size and complexity, that describes the structure of sleep data in clinically meaningful terms, and that will facilitates the discovery of patterns in sleep data using machine learning algorithms. We have interfaced our database with Weka, a well known data mining system. To the best of our knowledge, our database is one of the world's largest and most comprehensive in the domain of human sleep disorders.
Files	Shivin_Misra_thesis_2008.pdf