Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: A feasibility study

Computer Methods and Programs in Biomedicine

Volumn 78, Issue 3, 2005, Pages 191-207

  Ventouras, Errikos M ^a   Monoyiou, Efstratia A ^b   Ktonas, Periklis Y ^c   Paparrigopoulos, Thomas ^c   Dikeos, Dimitris G ^c   Uzunoglu, Nikos K ^b   Soldatos, Constantin R ^c  

^a UNIVERSITY OF WEST ATTICA   (Greece)

^b NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^c UNIVERSITY OF ATHENS   (Greece)

Author keywords

Artificial neural networks; EEG; Pattern recognition; Sleep spindles

Indexed keywords

BIOMEDICAL ENGINEERING; DYNAMICS; ELECTROENCEPHALOGRAPHY; FEATURE EXTRACTION; PHARMACODYNAMICS; TIME DOMAIN ANALYSIS;

MULTI-LAYER PERCEPTRON (MLP); SLEEP SPINDLE DETECTION; VISUAL ASSESSMENT;

NEURAL NETWORKS;

ACCURACY; ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; FEASIBILITY STUDY; FEMALE; HUMAN; HUMAN EXPERIMENT; MATHEMATICAL COMPUTING; SLEEP SPINDLE; TIME; VISION;

ALPHA RHYTHM; BETA RHYTHM; ELECTROENCEPHALOGRAPHY; FEASIBILITY STUDIES; GREECE; HUMANS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; SLEEP STAGES; SLEEP, REM;

EID: 19344375873     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2005.02.006     Document Type: Article

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