  In this paper we describe a new method combining the polynomial neural
network and decision tree techniques in order to derive comprehensible
classification rules from clinical electroencephalograms (EEGs) recorded from
sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement,
muscle and noise artifacts and as a consequence some EEG features are
irrelevant to classification problems. Combining the polynomial network and
decision tree techniques, we discover comprehensible classification rules
whilst also attempting to keep their classification error down. This technique
is shown to outperform a number of commonly used machine learning technique
applied to automatically recognize artifacts in the sleep EEGs.
