Dissertação
Uma metodologia para detecção de sonolência em tempo real com EEG vestível com duas derivações
Fecha
2020-02-20Autor
Cassenote, Vanessa
Institución
Resumen
Modern life often requires the subject to modify his circadian cycle due to his profession.
This change implies low performance, bad mood and, mainly, reduced attention and, drowsiness
at unsuitable times. According to the WHO, about 1.35 million people die each year
in traffic accidents. Thus, this work proposes a methodology for detecting drowsiness in
real time, with the aim of helping to minimize the problem mentioned. The proposed methodology
extracts Alpha, Theta, Beta and Gamma through the Haar Wavelet transform and
compares performances of the classifiers: MLP, KNN, LDA, RF, SVM and a Threshold. All
analyzes performed used signals from a public database. In order to be able to evaluate
the methodology in a more realistic environment, part of the signals were separated and
through an experiment, they were acquired by a wearable EEG. From there, the acquired
signal and the classifier performances for these signals were analyzed. In addition, the
performances of the classifiers for all samples (without EEG aquisition) and different epoch
sizes were also evaluated, being a 10 s epoch with a sliding window of 3 s the best, where
a sensitivity of 95% was obtained with an SVM.