Automatic detection of cyclic alternating pattern

  • Fábio Mendonça
  • Ana Fred
  • Sheikh Shanawaz Mostafa
  • Fernando Morgado-Dias
  • Antonio G. Ravelo-García
S.I. : Advances in Bio-Inspired Intelligent Systems
  • 53 Downloads

Abstract

The cyclic alternating pattern is a microstructure phasic event, present in the non-rapid eye movement sleep, which has been associated with multiple pathologies, and is a marker of sleep instability that is detected using the electroencephalogram. However, this technique produces a large quantity of information during a full night test, making the task of manually scoring all the cyclic alternating pattern cycles unpractical, with a high probability of miss classification. Therefore, the aim of this work is to develop and test multiple algorithms capable of automatically detecting the cyclic alternating pattern. The employed method first analyses the electroencephalogram signal to extract features that are used as inputs to a classifier that detects the activation (A phase) and quiescent (B phase) phases of this pattern. The output of the classifier was then applied to a finite state machine implementing the cyclic alternating pattern classification. A systematic review was performed to determine the features and classifiers that could be more relevant. Nine classifiers were tested using features selected by a sequential feature selection algorithm and features produced by principal component analysis. The best performance was achieved using a feed-forward neural network, producing, respectively, an average accuracy, sensitivity, specificity and area under the curve of 79, 76, 80% and 0.77 in the A and B phases classification. The cyclic alternating pattern detection accuracy, using the finite state machine, was of 79%.

Keywords

Automatic classification CAP A phase 

Notes

Acknowledgements

The authors acknowledge the Portuguese Foundation for Science and Technology for their support through Projeto Estratégico LA 9—UID/EEA/50009/2013 and also the ARDITI—Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação under the scope of the Project M1420-09-5369-FSE-000001—Ph.D. Studentship.

Compliance with ethical standards

Conflict of interest

All authors declare that they do not have conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Madeira Interactive Technologies InstituteFunchalPortugal
  2. 2.Instituto Superior Técnico - Universidade de LisboaLisbonPortugal
  3. 3.Instituto de TelecomunicaçõesInstituto Superior Técnico - Universidade de LisboaLisbonPortugal
  4. 4.Faculdade de Ciências Exatas e da Engenharia, Universidade da MadeiraFunchalPortugal
  5. 5.Institute for Technological Development and Innovation in CommunicationsUniversidad de Las Palmas de Gran CanariaLas PalmasSpain

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