Predicting the vulnerability to sleep deprivation

We all know that sleep deprivation can adversely affect the brain and cognitive function. We are slower at responding, making more mistakes, etc. Yet, some people are more vulnerability to sleep deprivation than others. The project aim is to answer such difference by predicting the vulnerability of...

Full description

Saved in:
Bibliographic Details
Main Author: Nguyen, Thuy Trang.
Other Authors: Vitali Zagorodnov
Format: Final Year Project
Language:English
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/10356/52863
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-52863
record_format dspace
spelling sg-ntu-dr.10356-528632023-03-03T20:45:02Z Predicting the vulnerability to sleep deprivation Nguyen, Thuy Trang. Vitali Zagorodnov School of Computer Engineering DRNTU::Engineering We all know that sleep deprivation can adversely affect the brain and cognitive function. We are slower at responding, making more mistakes, etc. Yet, some people are more vulnerability to sleep deprivation than others. The project aim is to answer such difference by predicting the vulnerability of a person with sleep deprivation from the reaction times collected from Psychomotor Vigilance Task which is a sustained-attention reaction time task that measure the time that subjects response to a visual stimulus. For each set of RT collected from a subject, there is a combination of four parameters that parameterize the data. The four parameters are mean and standard deviation of decision time and non-decision time of the subject that generates the RT set. An estimator has been developed to estimate those parameters from a RT data set using mean square error technique. This method is to try to minimize the difference of the probability density function(pdf) of observed RT and the pdf of simulated RT, which was simulated from the initial guess and adjusted parameters during estimating process. An evaluation metric for the estimator which is the Cramer-Rao Lower bound is developed to evaluate the accuracy of the estimator. Cramer-Rao Lower bound is the minimum variance of an unbiased estimator. The efficient of the estimator which could tell how close of the estimator could be approach to minimum variance can be figured out. With the reliable evaluation metrics, the estimator would be evaluated accurately and hence would be more reliable. Bachelor of Engineering (Computer Science) 2013-05-28T08:30:21Z 2013-05-28T08:30:21Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/52863 en Nanyang Technological University 46 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Nguyen, Thuy Trang.
Predicting the vulnerability to sleep deprivation
description We all know that sleep deprivation can adversely affect the brain and cognitive function. We are slower at responding, making more mistakes, etc. Yet, some people are more vulnerability to sleep deprivation than others. The project aim is to answer such difference by predicting the vulnerability of a person with sleep deprivation from the reaction times collected from Psychomotor Vigilance Task which is a sustained-attention reaction time task that measure the time that subjects response to a visual stimulus. For each set of RT collected from a subject, there is a combination of four parameters that parameterize the data. The four parameters are mean and standard deviation of decision time and non-decision time of the subject that generates the RT set. An estimator has been developed to estimate those parameters from a RT data set using mean square error technique. This method is to try to minimize the difference of the probability density function(pdf) of observed RT and the pdf of simulated RT, which was simulated from the initial guess and adjusted parameters during estimating process. An evaluation metric for the estimator which is the Cramer-Rao Lower bound is developed to evaluate the accuracy of the estimator. Cramer-Rao Lower bound is the minimum variance of an unbiased estimator. The efficient of the estimator which could tell how close of the estimator could be approach to minimum variance can be figured out. With the reliable evaluation metrics, the estimator would be evaluated accurately and hence would be more reliable.
author2 Vitali Zagorodnov
author_facet Vitali Zagorodnov
Nguyen, Thuy Trang.
format Final Year Project
author Nguyen, Thuy Trang.
author_sort Nguyen, Thuy Trang.
title Predicting the vulnerability to sleep deprivation
title_short Predicting the vulnerability to sleep deprivation
title_full Predicting the vulnerability to sleep deprivation
title_fullStr Predicting the vulnerability to sleep deprivation
title_full_unstemmed Predicting the vulnerability to sleep deprivation
title_sort predicting the vulnerability to sleep deprivation
publishDate 2013
url http://hdl.handle.net/10356/52863
_version_ 1759854263099457536