Automatic Sleep stage Scoring Using Hybrid Neural Network and Rule-based Expert Reasoning / 대한의료정보학회지
Journal of Korean Society of Medical Informatics
;
: 79-86, 2000.
Artigo
em Coreano
| WPRIM
| ID: wpr-149556
ABSTRACT
In order to increase the performance of automatic sleep stage scoring, we propose a hybrid neural-network and rule-based expert system taking advantages of each system. The suggesting hybrid system comprises signal cleaning. feature extraction, event detection, rule-based sleep scoring and neural network classification. We selected segment based EEG features. the state of EOG. and EMG tone as a major feature set. With the extracted features, the rule-based expert system classities the sleep stages by symbolic reasoning. The scoring process of rule-based expert system comprises the single epoch reasoning based on the typical events and the multi-epoch adjusting when no events are detected. If the decision of rule-based expert system is uncertain, then these features are fed into the neural network. We used a two hidden layer feed forward network using error hack propagation algorithm. The agreement rate between human scorer and automatic algorithm were evaluated. The neural network supplements the shortcomings of rule-based system by dealing with exceptions of rules. The result shows that the compuational ol computational and symbolic intelligence is promising approach sleep signal anal) sis.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Fases do Sono
/
Sistemas Inteligentes
/
Classificação
/
Eletroencefalografia
/
Eletroculografia
/
Inteligência
Tipo de estudo:
Estudo prognóstico
Limite:
Humanos
Idioma:
Coreano
Revista:
Journal of Korean Society of Medical Informatics
Ano de publicação:
2000
Tipo de documento:
Artigo
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