Alzheimer's disease (AD) is neurodegenerative disorder that causes
memory loss and cognitive dysfunction. It affects one in five people
over the age of 80 and is distressing for both sufferers and their
families. A transitional stage between normal ageing and dementia
including AD is termed a mild cognitive impairment (MCI). Recent
studies have shown that people with MCI may convert to AD over time
although not all MCI cases progress to AD. Much research is now
focussing on early detection of AD and diagnosing an MCI that will
progress to AD to allow prompt treatment and disease management
before the neurons degenerate to a stage beyond repair. Hence, the
ability to obtain a method of identifying MCI is of great importance.
Virtual reality plays an important role in healthcare and offers
opportunities for detection of MCI. There are various studies that have
focused on detection of early AD using virtual environments, although
results remain limited. One significant drawback of these studies has
been their limited capacity to incorporate levels of difficulty to
challenge users' capability. Furthermore, at best, these studies have
only been able to discriminate between early AD and healthy elderly
with about 80% of overall accuracy.
As a result, a novel virtual simulation called Virtual Reality for
Early Detection of Alzheimer's Disease (VREAD) was developed.
VREAD is a quick, easy and friendly tool that aims to investigate
cognitive functioning in a group of healthy elderly participants and
those with MCI. It focuses on the task of following a route, since Topographical Disorientation (TD) is common in AD. An investigation was set up with two cohorts: non-elderly and elderly participants. The findings with regard to the non-elderly are important as they represent a first step towards implementation with elderly people. The results with elderly participants indicate that this simulation based assessment could provide a method for the detection of MCI since significant correlations between the virtual simulation and existing neuropsychological tests were found. In addition, the results proved that VREAD is comparable with well-known neuropsychological tests, such as Cambridge Neuropsychological Automated Test Battery, Paired Associate Learning (CANTAB PAL) and Graded Naming Test (GNT). Furthermore, analysis through the use of machine learning techniques with regard to the prediction of MCI also obtained encouraging results. This novel simulation was able to predict with about 90% overall accuracy using weighting function proposed to discriminate between MCI and healthy elderly. / Ministry of Higher Education, Malaysia and University Sultan Zainal Abidin, Malaysia (UNisZa)
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/5529 |
| Date | January 2012 |
| Creators | Shamsuddin, Syadiah Nor Wan |
| Contributors | Ugail, Hassan, Lesk, Valerie E. |
| Publisher | University of Bradford, School of Computing, Informatics and Media |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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