Automatic detection of EEG arousals
Title
Automatic detection of EEG arousalsAuthor(s)
Date
2016-04-27Citation
Isaac Fernández, Elena Hernández, Diego Alvarez, Vicente Moret-Bonillo. Automatic detection of EEG arousals, in: Proceedings ESANN 2016. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (24) pp. 235-240. 2016
Abstract
[Abstract] Fragmented sleep is commonly caused by arousals that can be
detected with the observation of electroencephalographic (EEG) signals.
As this is a time consuming task, automatization processes are required. A
method using signal processing and machine learning models, for arousal
detection, is presented. Relevant events are identified in the EEG signals
and in the electromyography, during the signal processing phase. After
discarding those events that do not meet the required characteristics, the
resulting set is used to extract multiple parameters. Several machine learning
models — Fisher’s Linear Discriminant, Artificial Neural Networks and
Support Vector Machines — are fed with these parameters. The final proposed
model, a combination of the different individual models, was used
to conduct experiments on 26 patients, reporting a sensitivity of 0.72 and
a specificity of 0.89, while achieving an error of 0.13, in the arousal events
detection.
Keywords
Fragmented sleep
Electroencephalographic signals
Signal processing
Machine learning models
Electromyography
Fisher’s linear discriminant
Artificial neural networks
Support vector machines
Electroencephalographic signals
Signal processing
Machine learning models
Electromyography
Fisher’s linear discriminant
Artificial neural networks
Support vector machines
Editor version
ISBN
978-287587027-8.