A neural-network technique to learn concepts from electroencephalograms.

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.