The classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome

Physiological Measurement

Volumn 31, Issue 3, 2010, Pages 375-394

  Marcos, J V ^a   Hornero, R ^a   Lvarez, D ^a   Nabney, I T ^b   Del Campo, F ^c   Zamarron C ^d  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b ASTON UNIVERSITY   (United Kingdom)

^c HOSPITAL UNIVERSITARIO RÍO HORTEGA   (Spain)

^d Instituto de Salud Carlos III   (Spain)

Author keywords

Bayesian inference; Maximum likelihood; Multilayer perceptron (MLP); Nocturnal pulse oximetry; Obstructive sleep apnoea syndrome (OSAS)

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES; MAXIMUM LIKELIHOOD; MULTILAYERS; NONINVASIVE MEDICAL PROCEDURES; OXIMETERS; SIGNAL ANALYSIS; SLEEP RESEARCH; SPECTRUM ANALYSIS;

BAYESIAN INFERENCE; MA XIMUM LIKELIHOODS; MAXIMUM-LIKELIHOOD; MULTILAYER PERCEPTRON; MULTILAYERS PERCEPTRONS; NOCTURNAL PULSE OXIMETRY; OBSTRUCTIVE SLEEP APNEA SYNDROME; OBSTRUCTIVE SLEEP APNOEA SYNDROME; PULSE OXIMETRY; PULSE-OXIMETRY;

MULTILAYER NEURAL NETWORKS;

EID: 77149120423     PISSN: 09673334     EISSN: 13616579     Source Type: Journal    
DOI: 10.1088/0967-3334/31/3/007     Document Type: Article

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