Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/40327
Title: SpO2 based sleep apnea detection using deep learning
Authors: Shanawaz Mostafa, Sheikh
Mendonça, Fabio
Morgado-Dias, F.
Ravelo García, Antonio 
UNESCO Clasification: 33 Ciencias tecnológicas
Keywords: Deep belief nets
Deep learning
Restricted boltzmann machines
Sleep apnea,
Unsupervised
Feature learning
Issue Date: 2017
Journal: Proceedings - INES
Conference: 21st IEEE International Conference on Intelligent Engineering Systems (INES) 
21st IEEE International Conference on Intelligent Engineering Systems, INES 2017 
Abstract: In a classical classification process, automatic sleep apnea detection involves creating and selecting the features, using prior knowledge, and apply them to a classifier. A different approach is applied in this paper, where a Deep Belief Network is used for feature extraction, without using domain-specific knowledge, and then the same network is used for classification of sleep apnea. The Deep Belief Network was created by stacking Restricted Boltzmann Machines. The first two layers are autoencoder type and the last layer is of soft-max type. The initial weights are calculated using unsupervised learning and, at the end, a supervised fine-tuning of the weights is performed. Two public databases, one with 8 subjects and other with 25 subjects, are tested using tenfold cross validation. The optimum number of hidden neurons of this problem is found using a search technique. The accuracy achieved from UCD database is 85.26\% and Apnea-ECG database is 97.64\%.
URI: http://hdl.handle.net/10553/40327
ISBN: 9781479976775
ISSN: 1562-5850
DOI: 10.1109/INES.2017.8118534
Source: Proceedings - INES [ISSN 1562-5850], 21st International Conference on Intelligent Engineering Systems (Larnaca, Cyprus), p. 000091-000096
Appears in Collections:Actas de congresos
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