Machine learning approach to classification of sleep electroencephalograms from newborns at risk of brain pathologies

Abstract

This paper analyzes the Machine Learning approach to classifying sleep electroencephalograms recorded from newborns at risk of brain pathologies. The newborns were in different age groups counted in weeks of post-conceptional age. We consider solutions of the EEG task as a multiclass problem which can be resolved with Decision Tree models, which efficiently predict the weeks of PCA in terms of accuracy. The efficient solution to the multiclass tasks is difficult to find as decision models have to be explored in an ample model parameter space. Moreover, the sleep EEGs have significant overlap between ages because of variations in the newborn maturation patterns and expert evaluations. The experimental results have demonstrated the ability of ML technologies to provide classification accuracy comparable with the expert's opinions.

Hybrid convolutional-multilayer perceptron artificial neural network for person recognition by high gamma EEG features

“…If the length of the sequences in a study can be set fixed, convolutional neural networks (CNN) are more efficient in capturing sequence length because they can be easily trained parallelly [28][29][30]. Analyzing EGG records in such fixed-length fragments using CNN and hybrid architectures is another popular ML approach [4,9,19,20,32,36]. Input in CNN is expected as a pseudo-image, in which the duration of the EEG record is considered as a height of the «image,» and EEG channels are considered either as the «width» or pseudo-color channels.…”