Automatic Sleep stage Scoring Using Hybrid Neural Network and Rule-based Expert Reasoning.
- Author:
Hae Jeong PARK
1
;
Kwang Suk PARK
;
Do Un JEONG
Author Information
1. Institute of Biomedical Engineering, Seoul National University, College of Medicine, Korea. kspark@snuvh.snu.ac.kr
- Publication Type:Original Article
- Keywords:
Sleep scoring;
Rule-Based Reasong;
Neural Network
- MeSH:
Classification;
Electroencephalography;
Electrooculography;
Expert Systems;
Humans;
Intelligence;
Sleep Stages*
- From:Journal of Korean Society of Medical Informatics
2000;6(1):79-86
- CountryRepublic of Korea
- Language:Korean
-
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.
