On the analysis of BIS stage epochs via fuzzy clustering.

Among various types of clustering methods, partition-based methods such as k-means and FCM are widely used in the analysis of such data. However, when duration between stimuli is different, such methods are not able to provide satisfactory results because they find equal size clusters according to the fundamental running principle of these methods. In such cases, neighborhood-based clustering methods can give more satisfactory results because measurement series are separated from one another according to dramatic breaking points. In recent years, bispectral index (BIS) monitoring, which is used for monitoring the level of anesthesia, has been used in sleep studies. Sleep stages are classically scored according to the Rechtschaffen and Kales (R&K) scoring system. BIS has been shown to have a strong correlation with the R&K scoring system. In this study, fuzzy neighborhood/density-based spatial clustering of applications with noise (FN-DBSCAN) that combines speed of the DBSCAN algorithm and robustness of the NRFJP algorithm is applied to BIS measurement series. As a result of experiments, we can conclude that, by using BIS data, the FN-DBSCAN method estimates sleep stages better than the fuzzy c-means method.