A neural-network technique to learn concepts from electroencephalograms |
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Authors: | Email author" target="_blank">Vitaly?SchetininEmail author Email author" target="_blank">Joachim?SchultEmail author |
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Institution: | (1) Computer Science Department, University of Exeter, EX4 4QF Exeter, UK;(2) Friedrich-Schiller-University of Jena, Jena, Germany;(3) Moltpestr. 27, 23564 Luebeck, Germany |
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Abstract: | A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms (EEGs). A desired
concept is represented as a neuronal computational model consisting of the input, hidden, and output neurons. In this model
the hidden neurons learn independently to classify the EEG segments presented by spectral and statistical features. This technique
has been applied to the EEG data recorded from 65 sleeping healthy newborns in order to learn a brain maturation concept of
newborns aged between 35 and 51 weeks. The 39,399 and 19,670 segments from these data have been used for learning and testing
the concept, respectively. As a result, the concept has correctly classified 80.1% of the testing segments or 87.7% of the
65 records. |
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Keywords: | Artificial neural network Machine learning Decision tree Electroencephalogram |
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