Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-032608-125908


Document Typethesis
Author NameMisra, Shivin Satyawon
Email Address shivinm at gmail.com
URNetd-032608-125908
TitleA Database For Exploratory Analysis of Human Sleep
DegreeMS
DepartmentComputer 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/Defense2008-03-21
    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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