Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/77400
Title: Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection
Authors: Mostafa, Sheikh Shanawaz
Mendonca, Fabio
Ravelo García, Antonio Gabriel 
Julia-Serda, Gabriel
Morgado-Dias, Fernando
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Biomedical signal processing
Genetic algorithms
Machine intelligence
Medical expert systems
Pareto optimization, et al
Issue Date: 2020
Project: UID/EEA/50009/2019
M1420-01-0145-FEDER-000002
Journal: IEEE Access 
Abstract: Obstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted breathing during sleep. Because of the cost, complexity, and accessibility issue related to polysomnography, the gold standard test for apnea detection, automation of the diagnostic test based on a simpler method is desired. Several signals can be used for apnea detection, such as air ow and electrocardiogram. However, the reduction of air ow normally leads to a decrease in the blood oxygen saturation level (SpO2). This signal is usually measured by a pulse oximeter, a sensor that is cheap, portable, and easy to assemble. Therefore, the SpO2 was chosen as the reference signal. Feature based classi ers with shallow neural networks have been developed to provide apnea detection using SpO2. However, two main issues arise, the need for feature creation and the selection of the more relevant features. Deep neural networks can solve these issues by employing featureless methods. Among multiple deep classi ers that have been developed, convolution neural networks (CNN) are gaining popularity. However, the selection of the CNN structure and hyperparameters are typically done by experts, where prior knowledge is essential. With these problems in mind, an algorithm for automatic structure selection and hyper parameterization of a one dimension CNN was developed to detect OSA events using only the SpO2 signal. Three different input sizes and databases were tested, and the best model achieved an average accuracy, sensitivity, and speci city of 94%, 92%, and 96%, respectively.
URI: http://hdl.handle.net/10553/77400
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3009149
Source: IEEE Access [ISSN 2169-3536], n. 8, p. 129586-129599
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