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SC3: self-configuring classifier combination for obstructive sleep apnea

  • Sheikh Shanawaz MostafaEmail author
  • Fábio Mendonça
  • Gabriel Juliá-Serdá
  • Fernando Morgado-Dias
  • Antonio G. Ravelo-García
Developing nature-inspired intelligence by neural systems
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Abstract

Obstructive sleep apnea is considered to be one of the most prevalent sleep-related disorders that can affect the general population. However, the gold standard for the diagnosis, polysomnography, is an expensive and complicated process that is commonly unavailable to a large group of the population. Alternatively, automatic approaches have been developed to address this issue. One of the goals of this research is to perform the classification of the apnea events with the lowest possible number of sensors. Therefore, the blood oxygen saturation signal was employed in this work since it is correlated with the occurrence of apnea events and it can be measured from a single noninvasive sensor. The events detection was performed by a combination of classifiers. However, choosing the type of classifier to combine and select the most relevant features for each classifier is considered to be a well-known problem in the field of machine learning. A self-configuring classifier combination technique based on genetic algorithms was developed for multiple classifiers and features selection which was tested along with different databases and input sizes. The best performance for obstructive sleep apnea detection was achieved using maximum voting independent feature selection with 1 min time window having the best sensitivity of 82.48% similar database in the literature. This model was later tested on another database for cross-database accuracy. With an average accuracy of 91.33%, the system proved its capabilities for clinical diagnosis since the model was developed and validated with both subject and database independence.

Keywords

Combined classifiers Sleep apnea Genetic algorithm Machine learning 

Notes

Acknowledgements

This research has been supported by the Portuguese Foundation for Science and Technology through Projeto Estratégico UID/EEA/50009/2019, ARDITI—Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação under the scope of the Project M1420-09-5369-FSE-000001-PhD Studentship and MITIExcell—Excelencia Internacional de IDT&I NAS TIC (Project Number M1420-01-01450FEDER0000002), provided by the Regional Government of Madeira.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Instituto Superior Técnico, University of LisbonLisbonPortugal
  2. 2.ITI/Larsys/Madeira Interactive Technologies InstituteFunchalPortugal
  3. 3.Pulmonary Medicine DepartmentHospital Universitario de Gran Canaria Dr. NegrınLas Palmas de Gran CanariaSpain
  4. 4.University of MadeiraFunchalPortugal
  5. 5.Institute for Technological Development and Innovation in CommunicationsUniversity of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain

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