Automated prediction of the apnea-hypopnea index from nocturnal oximetry recordings

IEEE Transactions on Biomedical Engineering

Volumn 59, Issue 1, 2012, Pages 141-149

  Marcos, J Vctor ^a   Hornero, Roberto ^a   Álvarez, Daniel ^a   Aboy, Mateo ^b   Campo, Félix Del ^c  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b OREGON INSTITUTE OF TECHNOLOGY   (United States)

^c HOSPITAL UNIVERSITARIO RÍO HORTEGA   (Spain)

Author keywords

Apnea hypopnea index; multilayer perceptron neural networks; multiple linear regression; oxygen saturation; regression analysis; sleep apnea hypopnea syndrome

Indexed keywords

APNEA-HYPOPNEA INDEX; MULTILAYER PERCEPTRON NEURAL NETWORKS; MULTIPLE LINEAR REGRESSIONS; OXYGEN SATURATION; SLEEP APNEA-HYPOPNEA SYNDROME;

LINEAR REGRESSION; MULTILAYERS; OXYGEN; RATING; REGRESSION ANALYSIS; STATISTICS; TIME DOMAIN ANALYSIS;

MULTILAYER NEURAL NETWORKS;

ACCURACY; ADULT; ALGORITHM; APNEA HYPOPNEA INDEX; ARTICLE; AUTOMATION; COST EFFECTIVENESS ANALYSIS; DISEASE SEVERITY; FEMALE; GOLD STANDARD; HUMAN; MAJOR CLINICAL STUDY; MALE; MULTIPLE LINEAR REGRESSION ANALYSIS; OXIMETRY; OXYGEN SATURATION; POLYSOMNOGRAPHY; QUANTITATIVE ANALYSIS; SIGNAL PROCESSING; SLEEP APNEA SYNDROME; VALIDATION PROCESS;

ALGORITHMS; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MALE; MIDDLE AGED; OXIMETRY; OXYGEN; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX; SLEEP APNEA SYNDROMES;

EID: 84555218046     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2011.2167971     Document Type: Article

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