Citation

Jalilifard A, Pizzolato EB, Jalilifard A, Brigante Pizzolato E, Pizzolato EB, Jalilifard A. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016; 2016: 820-824.

Copyright

(Copyright © 2016, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC.2016.7590827

PMID

28226623

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.

Language: en