On Classification of Sleep Mechanism EEG Data
Pei Ling Lai, Alfred Inselberg
Journal of Electronic Systems, Vol. 3, No. 3, 2013.
DOI: 
Link: http://www.dline.info/jes/fulltext/v3n3/3.pdf
Abstract:
We utilize a recent form of the Nested Cavities (abbr. NC) classifier [1] from which a powerful new classification approach emerged. In this application there are many outliers in the datasets [5] which judiciously removed. Further, working on the classification of Stage 3 we found wide dispersal in the data. After considerable experimentation we concluded that, between Stage2 and Stage3 some of the data is misclassified. By including some of the Stage2 data with values very close to those of Stage3 and forming a New-Stage 3 ALL nine of the measured variables have tight value ranges and the whole data set visually appears as a well-defined cluster. In turn, accurate classification rules are obtained which had not been possible for the original partition into stages. These findings are explained, motivated and analyzed here.