Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).
about
A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses.Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stageRandom Forests Are Able to Identify Differences in Clotting Dynamics from Kinetic Models of Thrombin GenerationPsychophysics and neuronal bases of sound localization in humans.The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis.Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areasReal-data comparison of data mining methods in prediction of diabetes in iran.Advantages and limitations of anticipating laboratory test results from regression- and tree-based rules derived from electronic health-record data.EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random foClassification of EEG signals using a multiple kernel learning support vector machineAutomated Classification to Predict the Progression of Alzheimer's Disease Using Whole-Brain Volumetry and DTIOccipital sources of resting-state alpha rhythms are related to local gray matter density in subjects with amnesic mild cognitive impairment and Alzheimer's diseaseMolecular neuropsychology: creation of test-specific blood biomarker algorithms.Combining graph and machine learning methods to analyze differences in functional connectivity across sex.A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.Abnormalities of Cortical Neural Synchronization Mechanisms in Subjects with Mild Cognitive Impairment due to Alzheimer's and Parkinson's Diseases: An EEG Study.Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG RhythmsIdentifying patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related ERPRandom forest to differentiate dementia with Lewy bodies from Alzheimer's disease.Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients.Evidence-based evaluation of diagnostic accuracy of resting EEG in dementia and mild cognitive impairment.Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer's Disease.Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks.Electroencephalographic rhythms in Alzheimer's disease.The Dissociation between Polarity, Semantic Orientation, and Emotional Tone as an Early Indicator of Cognitive Impairment.Using artificial neural networks in clinical neuropsychology: high performance in mild cognitive impairment and Alzheimer's disease.A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares.Cerebrospinal Fluid Biomarkers of Alzheimer's Disease Correlate With Electroencephalography Parameters Assessed by Exact Low-Resolution Electromagnetic Tomography (eLORETA).Neurophysiological assessment of Alzheimer's disease individuals by a single electroencephalographic marker.Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography.Cross-evidence for hypnotic susceptibility through nonlinear measures on EEGs of non-hypnotized subjects.EEG theta and alpha reactivity on opening the eyes in the diagnosis of Alzheimer's disease.Clinician's road map to wavelet EEG as an Alzheimer's disease biomarker.EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia.Diagnostic accuracy of statistical pattern recognition of electroencephalogram registration in evaluation of cognitive impairment and dementia.Improving Alzheimer's disease diagnosis with machine learning techniques.Clinical detection of deletion structural variants in whole-genome sequences.Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis.
P2860
Q27313916-F2261A38-C6DB-45DB-84FF-AF725DB75612Q27500371-C39ABD40-0161-4663-A0CF-0B3AAAB1F91DQ28552035-42FA2625-B4B2-414E-9BAF-6BB188CF5A63Q30421745-0BAF8D6F-24A5-4F33-87B4-1706D6EDC8B6Q30440276-90BFA5C1-8FA1-470E-A7C8-9D16B6F1010CQ30443677-DF324D69-B236-4BDC-94A5-316B8DFCC017Q30686461-62D2F14E-D4DC-4D85-A2DF-F9A4CF9070FEQ30800528-A5116319-964E-4B14-9862-102F528F8229Q33915806-AB00904F-5051-4B12-82FA-862661E44253Q33995156-FA06EDF2-79F8-48E9-9D59-F1A38BDE1F60Q34210176-B390D710-C0DB-43E7-A8DC-8B87BCEA6A09Q35028381-26DDC8A0-01CD-43F6-9B53-676C32F731CAQ35044402-E442FE86-3730-486B-80AB-C77CE93C6502Q35456265-39CCD758-DED6-4958-AD55-0AC8DD3411DAQ35730971-E4259C46-11D8-4D60-9A37-45A722C9DD75Q36348208-84874F17-22B3-40F9-9EAD-A6F5C7844CD7Q36407197-B3748201-46D1-4CAA-BF71-17E7072E8378Q36605435-720A6602-EF68-4E57-959E-70234A2E07A3Q37286239-7E1B3FA2-0790-4088-B12C-18C028ED1C63Q37310159-DCC440F2-CE2E-405B-9B5C-AE9D08E792F5Q37452576-82DF1857-BB09-4FED-A3A0-8E483CEA0593Q37522373-D93DD94C-A2BF-4B91-B6EF-4DC3A2D62F40Q37547714-0A434078-80D2-41A4-BDF8-A18D2134F7E9Q37607161-F30556FA-233E-4F98-B661-66EC102F4619Q37883098-EF9B320C-C9FB-4E85-9968-70CEFB072BC1Q38387052-682E07D3-B21B-43B0-9A6B-F516DC281CC4Q38479582-F37C9815-B971-46EB-9376-68A1398A89C7Q38681607-6A3E3384-B443-4944-90E7-4342714D5411Q39447063-78A4063D-02D4-4464-94C3-5B3A245DD6F4Q39497929-100A2C2C-6EC4-4AC7-9654-BE7401F9E51EQ41253355-2EAC25AE-9595-4E70-800B-85CFC0943DD0Q41684046-C2C1D89C-11DA-44C4-8B26-1160EF84F61BQ41788257-3FE46693-D73A-4377-AD2B-1BDC6F0E2284Q42614140-7399ADF4-6E60-45BB-85AC-BF3D6CBEEDF3Q42623662-F7F9C84B-E784-4F02-A2FF-8860666F7823Q42936632-92804271-6471-4DD8-B9DC-8414BA43120CQ43480039-9CCC757E-3837-4D6D-9AC7-55E4171682B1Q45961598-CD6C8231-A30A-461C-AFD7-C22C26D8D6C9Q47128047-912CD37F-A665-4D59-B7AC-9A8D44DC3574Q47366765-B80C5D2C-D5C3-404D-932C-5E0521233847
P2860
Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).
description
2006 nî lūn-bûn
@nan
2006年の論文
@ja
2006年学术文章
@wuu
2006年学术文章
@zh
2006年学术文章
@zh-cn
2006年学术文章
@zh-hans
2006年学术文章
@zh-my
2006年学术文章
@zh-sg
2006年學術文章
@yue
2006年學術文章
@zh-hant
name
Application and comparison of ...... ectrical brain activity (EEG).
@en
Application and comparison of ...... e in electrical brain activity
@nl
type
label
Application and comparison of ...... ectrical brain activity (EEG).
@en
Application and comparison of ...... e in electrical brain activity
@nl
prefLabel
Application and comparison of ...... ectrical brain activity (EEG).
@en
Application and comparison of ...... e in electrical brain activity
@nl
P2093
P50
P1476
Application and comparison of ...... ectrical brain activity (EEG).
@en
P2093
Christoph Lehmann
Leslie Prichep
Roy E John
Vesna Jelic
Yadolah Dodge
P304
P356
10.1016/J.JNEUMETH.2006.10.023
P577
2006-12-06T00:00:00Z