Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/37168
Title: A minimally invasive portable system for sleep apnea detection
Authors: Mendonça, Fabio
Mostafa, Sheikh Shanawaz
Morgado-Dias, Fernando
Navarro Mesa, Juan Luis 
Juliá Serdá, José Gabriel
Ravelo-García, Antonio 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Feature selection
Hardware implementation
Oxygen saturation
Sleep apnea
Issue Date: 2017
Journal: 2017 International Work Conference On Bio-Inspired Intelligence: Intelligent Systems For Biodiversity Conservation, Iwobi 2017 - Proceedings
Conference: 5th IEEE International Work Conference on Bio-Inspired Intelligence, IWOBI 2017 
Abstract: Health care is changing the focus from primary and specialty care to prevention and wellness. Therefore, home health care is seen as one of the most relevant wellness services due to high accessibility and low cost of diagnosis. The growth relevance given to the sleep related disorders, due to the high importance of sleep in our lives, is specifically significant in this context encouraging the development of methods capable of non-invasively monitor and detection. One of the most relevant sleep disorders is obstructive sleep apnea, being the focus of the work presented in this paper to develop a minimally invasive portable system to detect this disorder using only oximetry. The system developed in this work is capable of collecting the oxygen saturation and pulse rate signals and send them wirelessly to the processing station where an application records and analyses the data. A graphical user interface guides the patients to start the monitoring session and a report is produced at the end of the analysis. The information is graphically presented to the patients and a resume file is generated to be analysed by the sleep technician. A database with 35 patients recordings was analysed, using a cross validation technique in order to evaluate the performance using a logistic regression model as a classifier. The algorithm achieved an accuracy of 86.6% (sensitivity = 66.9%, specificity = 94.5%, AUC = 90.7).
URI: http://hdl.handle.net/10553/37168
ISBN: 9781538608500
DOI: 10.1109/IWOBI.2017.7985540
Source: 2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedings[EISSN ], (Julio 2017)
Appears in Collections:Actas de congresos
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