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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:571808 |
| Date | January 2012 |
| Creators | Shamsuddin, Syadiah Nor Wan |
| Contributors | Ugail, Hassan; Lesk, Valerie |
| Publisher | University of Bradford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10454/5529 |
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