The Automatic Method of EEG State Classification by Using Self-Organizing Map
In psychiatry, the sleep stage is one of the most important evidence for diagnosing mental disease. However, when doctor diagnose the sleep stage, much labor and skill are required, and a quantitative and objective method is required for more accurate diagnosis. For this reason, an automatic diagnosis system must be developed. In this paper, we propose an automatic sleep stage diagnosis method by using Self Organizing Maps (SOM). Neighborhood learning of SOM makes input data which has similar feature output closely. This function is effective to understandable classifying of complex input data automatically. We applied Elman-type feedback SOM to EEG of not only normal subjects but also subjects suffer from disease. The spectrum of characteristic waves in EEG of disease subjects is often different from it of normal subjects. So, it is difficult to classifying EEG of disease subjects with the rule for normal subjects. On the other hand, Elman-type feedback SOM Classifies the EEG with features which data include and classifying rule is made automatically, so even the EEG with disease subjects is able to be classified automatically. And this Elman-type feedback SOM has context units for diagnosing sleep stages considering contextual information of EEG. Experimental results indicate that the proposed method is able to achieve sleep stage judgment along with doctor's diagnosis.