Deep learning representation from electroencephalography of early-stage creutzfeldt-jakob disease and features for differentiation from rapidly progressive dementia

International Journal of Neural Systems

Volumn 27, Issue 2, 2017, Pages

  Morabito, Francesco Carlo ^a   Campolo, Maurizio ^a   Mammone, Nadia ^f   Versaci, Mario ^a   Franceschetti, Silvana ^b   Tagliavini, Fabrizio ^b   Sofia, Vito ^c   Fatuzzo, Daniela ^c   Gambardella, Antonio ^d   Labate, Angelo ^d   Mumoli, Laura ^d   Tripodi, Giovanbattista Gaspare ^e   Gasparini, Sara ^d,e   Cianci, Vittoria ^e   Sueri, Chiara ^e   Ferlazzo, Edoardo ^d,e   Aguglia, Umberto ^d,e  

^a UNIVERSITY MEDITERRANEA OF REGGIO CALABRIA   (Italy)

^b DEPARTMENT OF NEUROSURGERY   (Italy)

^c UNIVERSITY OF CATANIA   (Italy)

^d UNIVERSITY MAGNA GRAECIA OF CATANZARO   (Italy)

^e BIANCHI MELACRINO MORELLI HOSPITAL   (Italy)

^f IRCCS CENTRO NEUROLESI BONINO PULEJO   (Italy)

Author keywords

Alzheimer's disease; CJD; classification; continuous wavelet transform; deep learning; dementia; EEG; subacute encephalopathies; SVM

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; MULTILAYERS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES; WAVELET TRANSFORMS;

ALZHEIMER'S DISEASE; CONTINUOUS WAVELET TRANSFORM; DEEP LEARNING; DEMENTIA; SUBACUTE ENCEPHALOPATHIES;

NEURODEGENERATIVE DISEASES;

AGED; ALZHEIMER DISEASE; BRAIN; CLINICAL TRIAL; CREUTZFELDT-JAKOB SYNDROME; DIFFERENTIAL DIAGNOSIS; DISEASE COURSE; ELECTROENCEPHALOGRAPHY; FEMALE; HUMAN; MALE; MIDDLE AGED; MULTICENTER STUDY; PATHOPHYSIOLOGY; PROCEDURES; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS;

AGED; ALZHEIMER DISEASE; BRAIN; CREUTZFELDT-JAKOB SYNDROME; DIAGNOSIS, DIFFERENTIAL; DISEASE PROGRESSION; ELECTROENCEPHALOGRAPHY; FEMALE; HUMANS; MALE; MIDDLE AGED; RETROSPECTIVE STUDIES; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS;

EID: 85007400475     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065716500398     Document Type: Article

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