Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules

International Journal of Adaptive Control and Signal Processing

Volumn 24, Issue 5, 2010, Pages 409-423

  Chapotot, Florian ^a,c   Becq, Guillaume ^b,c  

^a UNIVERSITY OF CHICAGO   (United States)

^b GIPSA LAB   (France)

^c PhiTools   (France)

Author keywords

Data mining; Decision rule; Machine learning; Polysomnography; Signal processing; Sleep

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLASSIFICATION ACCURACY; CLASSIFICATION FRAMEWORK; CLASSIFICATION PERFORMANCE; DECISION RULES; EMG SIGNAL; ERROR RATE; FEATURE SETS; INDIVIDUAL VARIABILITY; MACHINE-LEARNING; MULTIPLE LAYERS; NON-LINEAR; OPTIMAL SETS; POLYSOMNOGRAPHIC SIGNALS; POLYSOMNOGRAPHY; SCORING CRITERIA; SEQUENTIAL FORWARD SEARCH; SIMPLE MAJORITY; SLEEP STAGE; SLEEP STAGING;

CLASSIFIERS; LEARNING SYSTEMS; MEDICAL APPLICATIONS; NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; SIGNAL PROCESSING; SLEEP RESEARCH; WAKES;

FEATURE EXTRACTION;

EID: 77951616783     PISSN: 08906327     EISSN: 10991115     Source Type: Journal    
DOI: 10.1002/acs.1147     Document Type: Article

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