Classification algorithms for predicting sleepiness and sleep apnea severity

Journal of Sleep Research

Volumn 21, Issue 1, 2012, Pages 101-112

  Eiseman, Nathaniel A ^a   Westover, M Brandon ^a   Mietus, Joseph E ^b   Thomas, Robert J ^b,c   Bianchi, Matt T ^a,c  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b BETH ISRAEL DEACONESS MEDICAL CENTER   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Correlation; Distribution; Mutual information

Indexed keywords

ADULT; APNEA HYPOPNEA INDEX; AROUSAL; ARTICLE; BAYESIAN LEARNING; BODY MASS; CLASSIFICATION ALGORITHM; CLINICAL FEATURE; CONTROLLED STUDY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE SEVERITY; ELECTROCARDIOGRAM; EPWORTH SLEEPINESS SCALE; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; OXYGENATION; POLYSOMNOGRAPHY; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SLEEP APNEA SYNDROME; SOMNOLENCE; SUPPORT VECTOR MACHINE;

ADULT; ALGORITHMS; DISORDERS OF EXCESSIVE SOMNOLENCE; ELECTROCARDIOGRAPHY; HUMANS; POLYSOMNOGRAPHY; PROGNOSIS; PSYCHOMETRICS; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX; SLEEP APNEA, OBSTRUCTIVE; SLEEP STAGES;

EID: 84856101628     PISSN: 09621105     EISSN: 13652869     Source Type: Journal    
DOI: 10.1111/j.1365-2869.2011.00935.x     Document Type: Article

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