The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns

2005/04/14 by Vitaly Schetinin, Schetinin, Vitaly, Joachim Schult +1
Computer Science · Neuroscience · #Blind Source Separation Techniques #EEG and Brain-Computer Interfaces #Neural Networks and Applications #cs.AI #cs.LG #cs.NE

paper · pdf · doi:10.48550/arxiv.cs/0504070

arxiv created 2005/04/14 · arxiv updated 2009/12/01

Abstract

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.

Citations

• A new method for off-line removal of ocular artifact

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