A Neural-Network Technique to Learn Concepts from Electroencephalograms

TL;DR

This paper introduces a neural-network based method for learning and classifying brain maturation concepts from EEG data of newborns, achieving high accuracy in segment classification.

Contribution

It presents a novel neural network approach that independently learns to classify EEG segments for understanding brain development in newborns.

Findings

Achieved 80.1% accuracy on segment classification.

Correctly classified 87.7% of individual records.

Applied to EEG data from 65 newborns aged 35-51 weeks.

Abstract

A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms. 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 electroencephalogram segments presented by spectral and statistical features. This technique has been applied to the electroencephalogram data recorded from 65 sleeping healthy newborns in order to learn a brain maturation concept of newborns aged between 35 and 51 weeks. The 39399 and 19670 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.