Date of Award

Fall 1994

Document Type

Thesis

Degree Name

Master of Science in Biomedical Engineering - (M.S.)

Department

Biomedical Engineering Committee

First Advisor

Joseph Frank

Second Advisor

Swamy Laxminarayan

Third Advisor

Kenneth Grasing

Fourth Advisor

David S. Kristol

Abstract

The physiological relationship between the various components of sleep and its variation due to drug administration has been used as one of the primary tools to analyze the performance of drug. A number of studies have been performed in recent years in this direction. Electroencephalogram (EEG) has been characterized with the help of variables ranging from measurements of the duration of different sleep stages to the activities that define the stages themselves. Advances in computer hardware and software have improved the methods of data acquisition and storage. Analysis of long stretch of data has always been a problem considering the time and storage.

The present study is aimed at characterizing sleep data from a subject suffering from neurologic disorder. It also aims at identifying the effect of Oxycodon a Narcotic drug on the subject during sleep. The data considered for analysis is the output of a whole night recording. It is for a duration of six hours. EEG signals are analyzed using random data analysis procedures. The assumption of stationarity will be used as the basis of analysis. However the fact that analysis on long stretch of data introducing nonstationarity will not be ruled out. The analysis will be performed using Fast Fourier Transformation. Spectral analysis will be used as the primary tool in identifying the activities of various frequency components and its variation with time. The three parameters that will be considered are Mean square values and correlation function in time domain, Spectral analysis application in frequency domain and the probability density and distribution functions in the amplitude domain. Algorithms will be developed for computing these parameters and other statistical properties.

Share

COinS