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http://hdl.handle.net/11434/1989
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bailey, Neil | - |
dc.contributor.author | Hoy, Kate | - |
dc.date | 2020-10-29 | - |
dc.date.accessioned | 2021-06-10T04:57:11Z | - |
dc.date.available | 2021-06-10T04:57:11Z | - |
dc.date.issued | 2021-01 | - |
dc.identifier.citation | 132 (1), pp.207-209 | en_US |
dc.identifier.issn | 1388-2457 | en_US |
dc.identifier.uri | http://hdl.handle.net/11434/1989 | - |
dc.description.abstract | Editorial discussing the retrospective and exploratory study by Ferri et al (2020) published in this issue, which tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer’s disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both. | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Artificial Neural Networks | en_US |
dc.subject | ANNs | en_US |
dc.subject | Alzheimer's disease | en_US |
dc.subject | Dementia | en_US |
dc.subject | ADD | en_US |
dc.subject | Resting-state Electroencephalogram | en_US |
dc.subject | rsEEG | en_US |
dc.subject | Structural Magnetic Resonance Imaging | en_US |
dc.subject | sMRI | en_US |
dc.subject | Epworth Centre for Innovation in Mental Health, Epworth Healthcare and Monash University Department of Psychiatry, Camberwell, Victoria | en_US |
dc.subject | Epworth Internal Medicine Clinical Institute, Epworth HealthCare, Victoria, Australia | en_US |
dc.subject | Neurosciences Clinical Institute | en_US |
dc.title | The promise of artificial neural networks, EEG, and MRI for Alzheimer's disease. | en_US |
dc.type | Journal Article | en_US |
dc.identifier.doi | 10.1016/j.clinph.2020.10.009 | en_US |
dc.identifier.journaltitle | Clinical Neurophysiology | en_US |
dc.description.pubmeduri | https://pubmed.ncbi.nlm.nih.gov/33176985/ | en_US |
dc.type.contenttype | Text | en_US |
Appears in Collections: | Neurosciences |
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