Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42207
Title: Automatic detection of a phases for CAP classification
Authors: Mendonça, Fabio
Fred, Ana
Shanawaz Mostafa, Sheikh
Morgado-Dias, Fernando
Ravelo-García, Antonio G. 
UNESCO Clasification: 33 Ciencias tecnológicas
Keywords: A Phase
Cyclic Alternating Pattern
CAP
LDA
Issue Date: 2018
Conference: 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM) 
Abstract: 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.
URI: http://hdl.handle.net/10553/42207
ISBN: 978-989-758-276-9
DOI: 10.5220/0006595103940400
Source: Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2018), p. 394-400, (Enero 2018)
Appears in Collections:Actas de congresos
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