Back to Search
Start Over
Analysis of vigilance states by neural networks
- Source :
- Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004..
- Publication Year :
- 2004
- Publisher :
- IEEE, 2004.
-
Abstract
- The main aim in this paper is to study an algorithm of vigilance detection from a minimal number of EEG electrodes, easy to implement on programmable devices, to be used in ambulatory and real everyday life conditions. The connectionist unsupervised approach is summarized in this paper. From the unsupervised classification obtained, a connectionist supervised classification algorithm, the learning vector quantization (LVQ), is used for two different tasks. Firstly, the artefacted states are detected and removed. Secondly, the states deprived of artefacts are then classified in order to decide for the state of vigilance. Connectionist methods with supervised and unsupervised training were used to discriminate the EEG signals characterizing the vigilance states. An artificial neuronal model with a minimal architecture minimizes the complexity and allows implementation. It demonstrates that information, pertinent enough to characterize vigilance states, can be extracted from EEG signal recorded from a single electrode It should also be noted that the intervention of the expert is fundamental in this approach to differentiate nonartefacted vigilance states and artefacted vigilance states.
- Subjects :
- Signal processing
Learning vector quantization
Artificial neural network
medicine.diagnostic_test
Computer science
business.industry
media_common.quotation_subject
Pattern recognition
Electroencephalography
Machine learning
computer.software_genre
Connectionism
medicine
Unsupervised learning
Detection theory
Artificial intelligence
business
computer
Vigilance (psychology)
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004.
- Accession number :
- edsair.doi...........7b9eb47c6335cc0bce4f272dbe6af7f1