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Publication
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection
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Advisor(s)
Abstract(s)
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 airflow and electrocardiogram. However,
the reduction of airflow 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 classifiers 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 classifiers 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 specificity of 94%, 92%, and
96%, respectively.
Description
Keywords
Biomedical signal processing CNN Genetic algorithms Machine intelligence Medical expert systems Pareto optimization Sleep apnea SpO2 . Faculdade de Ciências Exatas e da Engenharia
Citation
Mostafa, S. S., Mendonca, F., Ravelo-Garcia, A. G., Juliá-Serdá, G. G., & Dias, F. M. (2020). Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection. IEEE Access, 8, 129586-129599.
Publisher
IEEE