Authors:
Fabio Mendonça
1
;
Ana Fred
2
;
Sheikh Shanawaz Mostafa
1
;
Fernando Morgado-Dias
3
and
Antonio G. Ravelo-García
4
Affiliations:
1
Madeira Interactive Technologies Institute and Universidade de Lisboa, Portugal
;
2
Universidade de Lisboa, Portugal
;
3
Madeira Interactive Technologies Institute and Universidade da Madeira, Portugal
;
4
Universidad de Las Palmas de Gran Canaria, Spain
Keyword(s):
A Phase, Cyclic Alternating Pattern, CAP, LDA.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Feature Selection and Extraction
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
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.
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