The combined technique for detection of artifacts in clinical electroencephalograms of sleeping newborns.
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 while also attempting to keep their classification error down. This technique is shown to out-perform a number of commonly used machine learning technique applied to automatically recognize artifacts in the sleep EEGs.
Schetinin
Vitaly
V
Department of Computer Science, the University of Exeter, Exeter, EX4 4QF, UK. V.Schetinin@ex.ac.uk
Schult
Joachim
J
D000465:Algorithms; D016477:Artifacts; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D009068:Movement; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012890:Sleep / Q000502:physiology*