Sleeping with ants, SVMs, multilayer perceptrons and SOMs

This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired heuristics. Preliminary results show that some methods are able to attain success rates close to 70% when compared to an expert neurologist. Although still not sufficient to implement a reliable sleep classifier, these are promising results that, together with an analysis via Self-Organizing Maps and ant-based clustering, may help to improve the feature extraction and contribute to a better representation of the different classes' characteristics.