Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection
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S.I. : ADVANCES IN BIO-INSPIRED INTELLIGENT SYSTEMS
Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection Sheikh Shanawaz Mostafa1,2
•
Fernando Morgado-Dias2,3
•
Antonio G. Ravelo-Garcı´a4
Received: 12 January 2018 / Accepted: 23 March 2018 The Natural Computing Applications Forum 2018
Abstract Obstructive sleep apnea is a disorder characterized by pauses in respiration during sleep. Due to this disturbance in breathing, there is a decrease in the oxygen saturation (SpO2) level. Thus, SpO2 can be used as a source of information for the automatic detection of apnea. Several solutions exist in the literature where different features are used. To find a better discriminant capacity, a subset of few features that obtains higher accuracy with the proper classifier is needed. To face this challenge, this work compares two different feature selection methods. The first one is a filter method named minimum redundancy maximum relevance, and the other one is called sequential forward search. These methods are tested with different classifiers. Two public datasets with 8 and 25 subjects are used to test and compare the performances of the different feature selection methods. A set of features for each classifier is obtained, and the results are compared with the previous work. The results found in this work show a good performance with respect to the state of the art and present a good option for apnea screening with low resources. Keywords Classification Feature section mRMR SFS Sleep apnea SpO2
1 Introduction Obstructive sleep apnea (OSA) is a common sleep disorder with a high occurrence among 4% in adult men and 2% in adult women [1]. There are over 200 million sleep apnea patients all over the world [2]. Moreover, more than 80%
& Sheikh Shanawaz Mostafa [email protected] Fernando Morgado-Dias [email protected] Antonio G. Ravelo-Garcı´a [email protected] 1
Instituto Superior Te´cnico da Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
2
MITI - Madeira Interactive Technologies Institute, Caminho da Penteada, 9020-105 Funchal, Portugal
3
Universidade da Madeira, Praca do Municı´pio 17, 9000-034 Funchal, Portugal
4
Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain
of the apneic patients are not aware of the problem [3]. Polysomnography (PSG) is the gold standard to detect the bio-physiological changes that occur during sleep. It monitors different recordings as electroencephalogram (EEG), electrooculogram (EOG), electromyography (EMG) and electrocardiography (ECG) during sleep, and the number of the signals can increase according to the requirements. Managing a polysomnography is a tedious and time-consuming task requiring the analysis of multiple-signal channels [4]. There is a high economic cost associated due to the equipment m
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