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dc.contributor.authorPatient, Rebecca
dc.contributor.authorGhali, Fawaz
dc.contributor.authorKolivand, Hoshang
dc.contributor.authorHurst, William
dc.contributor.authorJohn, Nigel W.
dc.date.accessioned2021-12-10T10:52:08Z
dc.date.available2021-12-10T10:52:08Z
dc.date.issued2022-03-01
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/626560/2021232563.pdf?sequence=5
dc.identifier.citationPatient, R., Fawaz, G., Hoshang, K., Hurst, W. & John, N.W. (2021). Application of virtual reality and electrodermal activity for the detection of cognitive impairments. In 2021 14th International Conference on Developments in eSystems Engineering (pp. 156-161). Institute of Electrical and Electronics Engineers (IEEE).en_US
dc.identifier.isbn9781665408899
dc.identifier.doi10.1109/DeSE54285.2021.9719442
dc.identifier.urihttp://hdl.handle.net/10034/626560
dc.description© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.description.abstractMild Cognitive Impairment (MCI) is a definition of the diagnosis of early memory loss and disorientation. This study aims to identify people’s symptoms through technology. However, machine learning (ML) can classify Cognitive Normal (CN) and Mild Cognitive Impairment (MCI) and Early Mild Cognitive Impairment (EMCI) using standard assessments from the Alzheimer’s Disease Neuroimaging Initiative (ADNI); Montreal Cognitive (MoCA), Mini-Mental State Examination (MMSE), Functional Activities Questionnaire (FAQ). Consequently, a Multilayer Perceptron (MLP) model was assembled into tables; MCI vs CN, MCI vs EMCI, and CN vs MCI. Additionally, an MLP model was developed for CN vs MCI vs EMCI. As a result, of advanced model performance, a cascade 3-path categorisation approach was created. Similarly, the exploitation of meta-analysis indicated a combination of MLP models (MCI vs CN, MCI vs EMCI, and CN vs MCI) with an overall accuracy within an acceptable limit. In addition, better results were found when assessments were combined rather than individually. Furthermore, applying class weights and probability thresholds could improve the MLP framework by performance achieving a balanced specificity and sensitivity ratio. Altering class weights and probability thresholds when training the MLP neuro network model, the sensitivity and Accuracy could be progressed further. In conclusion, ML, VR and electrodermal activity are constrained. Introducing the possibility of activity-based applications to enhance innovative solutions for cognitive impairment diagnosis and treatment.en_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/xpl/conhome/1003085/all-proceedingsen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectMild Cognitive Impairmenten_US
dc.subjectMachine Learningen_US
dc.subjectNeuropsychological Assessmenten_US
dc.subjectVirtual Realityen_US
dc.subjectElectrodermal Activityen_US
dc.titleApplication of Virtual Reality and Electrodermal Activity for the Detection of Cognitive Impairmentsen_US
dc.typeConference Proceedingen_US
dc.contributor.departmentLiverpool John Moores; Wageningen University; University of Chesteren_US
dc.identifier.journal14th International Conference on Developments in eSystems Engineering (DeSE)en_US
or.grant.openaccessYesen_US
rioxxterms.funderunfundeden_US
rioxxterms.identifier.projectunfiundeden_US
rioxxterms.versionAMen_US
rioxxterms.licenseref.startdate2024-03-01
dcterms.dateAccepted2021-10-17
rioxxterms.publicationdate2022-03-01
dc.date.deposited2021-12-10en_US


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