Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41536
Title: Optimization of sleep apnea detection using SpO2 and ANN
Authors: Mostafa, Sheikh Shanawaz
Carvalho, Joao Paulo
Morgado-Dias, Fernando
Ravelo-García, Antonio 
UNESCO Clasification: 32 Ciencias médicas
Keywords: Classification
Feature Section
Sleep Apnea
SpO2
Issue Date: 2017
Conference: 26th International Conference on Information, Communication and Automation Technologies, ICAT 2017 
Abstract: Repetitive respiratory disturbance during sleep is called Sleep Apnea Hypopnea Syndrome and causes various diseases. Different features and classifiers have been used by different researchers to detect sleep apnea. This study is undertaken to identify the better performing blood oxygen saturation features subset using an Artificial Neural Network classifier for sleep Apnea detection. A database of 8 subjects with one-minute annotation is used to test the proposed system. The optimized system has seven features chosen from a total set of sixty-one features presenting a high accuracy rate using a genetic algorithm. Artificial Neural Network was able to achieve 97.7 percentage of accuracy with only seven features chosen by the Genetic algorithm.
URI: http://hdl.handle.net/10553/41536
ISBN: 9781538633373
DOI: 10.1109/ICAT.2017.8171609
Source: 2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo, pp. 1-6; Electronic ISBN: 978-1-5386-3337-3
Appears in Collections:Actas de congresos
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