Journal ArticleDOI
An intelligent system for the detection and interpretation of sleep apneas
M. Cabrero-Canosa,M Castro-Pereiro,M Graña-Ramos,Elena Hernández-Pereira,Vicente Moret-Bonillo,M Martin-Egaña,H Verea-Hernando +6 more
TLDR
A modular system based on artificial intelligence techniques that provides an individual SAS diagnosis on the basis of a patient's polysomnograph, and the results obtained following a preliminary validation of the developed system are presented.Abstract:
The sleep apnea syndrome (SAS) is a respiratory disorder, which is characterised by the occurrence of five or more apneic events (apnea or hypopnea) per hour of sleep. Diagnosis of the SAS is a process that is markedly heuristic by nature, in that doctors handle information that is both numerical and symbolic, and employ qualitative descriptive terminology. An expert draws up a contextualised clinical interpretation that relates a patient's sleep process and respiratory physiology, involving a detailed analysis of the polysomnograph corresponding to a night's sleep. This task, implying a great deal of work on the part of clinical staff and a high economic cost, can in fact be partially automated. Our paper describes a modular system based on artificial intelligence techniques that provides an individual SAS diagnosis on the basis of a patient's polysomnograph. The main tasks of our system are the identification and classification of respiratory events, the construction of the patient's hypnogram and the correlation of all the information obtained so as to arrive at a final diagnosis with respect to the existence of the syndrome. Finally our article presents and discusses the results obtained following a preliminary validation of the developed system.read more
Citations
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Journal ArticleDOI
A new method for sleep apnea classification using wavelets and feedforward neural networks
TL;DR: A feedforward neural network trained using the Bayesian framework and a cross-entropy error function is presented, which surpasses, up to the author's knowledge, other previous results for sleep apnea classification.
Journal ArticleDOI
Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome
TL;DR: A fuzzy-logic-based automated system for detecting apneic events and classifying them as apneas or hypopneas is described to equip this system with mechanisms for dealing with imprecision and reasoning affected by uncertainty.
Journal ArticleDOI
Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.
TL;DR: Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection and some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals.
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