ON THE USE OF NEURAL-NETWORK TECHNIQUES TO ANALYZE SLEEP EEG DATA - 3RD-COMMUNICATION - ROBUSTIFICATION OF THE CLASSIFICATOR BY APPLYING ANALGORITHM OBTAINED FROM 9 DIFFERENT NETWORKS

This is the third communication on the use of neural network technique s to classify sleep stages. In our first communication we presented th e algorithms and the selection of the feature space and its reduction by using evolutionary and genetic procedures. In our second communicat ion we trained the evolutionary optimized networks on the basis of mul tiple subject data in context with some smoothing algorithms in analog y of Rechtschaffen and Kales (RK). In this third communication we coul d demonstrate that the robustness concerning individual specific featu res of automatically generated sleep profiles could be reasonably impr oved by an additional modification of the procedure used by SASCIA (Sl eep Analysis System to Challenge Innovative Artificial Networks). The outputs of nine different networks that were created by the data of 9 different subjects were used simultaneously for classification. The me dians of the values obtained in each output measure were selected for the allocation to a sleep stage. The fitness criteria of 16 automatica lly generated sleep profiles showed reasonable concordance with the ex pert profile. Even though in single cases the concordance between conv entional RK classifications and automatically generated profiles were a few percentages lower, the average correct classification of the 12 classified subjects improved substantially, thus proving that the clas sifier is more robust against individuum-specific variability. Despite the fact that the expert generally employs three channels (EEG, EMG a nd EGG), at least to build up sleep profiles, the SASCIA system was ab le to produce profiles on the basis of only one EEG channel with 80% c oncordance and a correlation coefficient of 0.86. The feature selectio ns were performed by genetic algorithms and the topologies of the netw orks were optimized by evolutionary algorithms. This algorithm will no w be used for larger sample forward classification.