title: An efficient K-NN approach for automatic drowsiness detection using single-channel EEG recording.
abstract: Drowsy driving is a major cause of many traffic accidents. The aim of this work is to develop an automatic drowsiness detection system using an efficient k-nearest neighbors (K-NN) algorithm. First, the distribution of power in time-frequency space was obtained using short-time Fourier transform (STFT) and then, the mean value of power during time-segments of 0.5 second was calculated for each EEG subband. In addition, standard deviation (SD) and Shanon entropy related to each time-segment were computed from time-domain. Finally, 52 features were extracted. Random forest algorithm was applied over the extracted data, aiming to choose the most informative subset of features. A total of 11 features were selected in order to classify drowsiness and alertness. Kd-trees was used as the nearest neighbors search algorithm so as to have a fast classifier. Our experimental results show that drowsiness can be classified efficiently with 91% accuracy using the methods and materials proposed in this paper.
authors.forename: Amir
authors.initials: A
authors.lastname: Jalilifard
authors.forename: Ednaldo
authors.initials: E
authors.lastname: Brigante Pizzolato
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control; D000465:Algorithms; D001334:Automobile Driving; D004569:Electroencephalography; D006801:Humans; D012894:Sleep Stages