Hybrid neural-network and rule-based expert system for automatic sleep stage scoring

In order to increase the performance of automatic sleep stage scoring, we propose a hybrid neural-network and expert system taking advantages of each system. After signal cleaning and feature extraction from polysomnographic signals using several algorithms we suggested, the rule-based expert system classified the sleep states with symbolic reasoning. The neural network supplemented the shortcomings of rule-based system by dealing with exceptions of rules. The result shows that the combination of computational and symbolic intelligence is promising approach to automatic sleep signal analysis.