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Publication
Automatic Detection of a Phases for CAP Classification
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Advisor(s)
Abstract(s)
The aim of this study is to develop an automatic detector of the cyclic alternating pattern by first detecting
the activation phases (A phases) of this pattern, analysing the electroencephalogram during sleep, and then
applying a finite state machine to implement the final classification. A public database was used to test the
algorithms and a total of eleven features were analysed. Sequential feature selection was employed to select
the most relevant features and a post processing procedure was used for further improvement of the
classification. The classification of the A phases was produced using linear discriminant analysis and the
average accuracy, sensitivity and specificity was, respectively, 75%, 78% and 74%. The cyclic alternating
pattern detection accuracy was 75%. When comparing with the state of the art, the proposed method
achieved the highest sensitivity but a lower accuracy since the fallowed approach was to keep the REM
periods, contrary to the method that is used in the majority of the state of the art publications which leads to
an increase in the overall performance. However, the approach of this work is more suitable for automatic
system implementation since no alteration of the EEG data is needed.
Description
Keywords
A phase Cyclic alternating pattern CAP LDA . Escola Superior de Tecnologias e Gestão Faculdade de Ciências Exatas e da Engenharia
Citation
Mendonça, F.; Fred, A.; Shanawaz Mostafa, S.; Morgado-Dias, F. and Ravelo-García, A. (2018). Automatic Detection of a Phases for CAP Classification. In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-276-9; ISSN 2184-4313, SciTePress, pages 394-400. DOI: 10.5220/0006595103940400
Publisher
SciTePress