Automatic detection of sleep spindles by neural networks algorithms

Abstract

Sleep constitutes an essential aspect of human existence, with the average individual dedicating approximately one-third of their life to this physiological activity. Consequently, comprehending and accurately analyzing sleep patterns is of paramount importance. This research aims to introduce, formulate, execute, and assess diverse machine/deep learning methodologies tailored for the processing of EEG signals geared explicitly towards identifying sleep spindles. The learning algorithms underwent training using meticulously annotated data from the Montreal Archive of Sleep Studies (MASS) data center. The convolutional neural network emerged as the most effective classification model, achieving an accuracy surpassing 67 %.