title: An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of EEG records.
abstract: An algorithm to detect automatically drowsiness episodes has been developed. It uses only one EEG channel to differentiate the stages of alertness and drowsiness. In this work the vectors features are building combining Power Spectral Density (PDS) and Wavelet Transform (WT). The feature extracted from the PSD of EEG signal are: Central frequency, the First Quartile Frequency, the Maximum Frequency, the Total Energy of the Spectrum, the Power of Theta and Alpha bands. In the Wavelet Domain, it was computed the number of Zero Crossing and the integrated from the scale 3, 4 and 5 of Daubechies 2 order WT. The classifying of epochs is being done with neural networks. The detection results obtained with this technique are 86.5 % for drowsiness stages and 81.7% for alertness segment. Those results show that the features extracted and the classifier are able to identify drowsiness EEG segments.
authors.affiliation: Gabinete de Tecnología Medica, Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), Argentina. agarces@gateme.unsj.edu.ar
authors.forename: Agustina
authors.initials: A
authors.lastname: Garces Correa
authors.forename: Eric
authors.initials: E
authors.lastname: Laciar Leber
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012894:Sleep Stages / Q000502:physiology*; D058067:Wavelet Analysis; D055815:Young Adult