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, m...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τόπος έκδοσης:	IEEE Transactions on information technology in biomedicine 8, 1 (2004).
Κύριος συγγραφέας:	Schetinin, V.
Μορφή:	Άρθρο
Γλώσσα:	English
Θέματα:	Artifacts detection. Cardiac. Classification rules. Clinical electroencephalograms. Decision tree techniques. Eye movement. Feature evaluation. Feature selection. Machine learning technique. Mining algorithms. Mining methods. Muscle. Noise artifacts. Polynomial neural network. Sleeping newborns.