Causal probabilistic network and power spectral estimation used in sleep stage classification.
Methods Inf Med
; 36(4-5): 345-8, 1997 Dec.
Article
em En
| MEDLINE
| ID: mdl-9470395
ABSTRACT
A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fases do Sono
/
Processamento de Sinais Assistido por Computador
/
Modelos Estatísticos
/
Polissonografia
/
Modelos Biológicos
Tipo de estudo:
Risk_factors_studies
Limite:
Adult
/
Humans
Idioma:
En
Revista:
Methods Inf Med
Ano de publicação:
1997
Tipo de documento:
Article
País de afiliação:
Dinamarca