TY  - JOUR
PY  - 2016//
TI  - An efficient K-NN approach for automatic drowsiness detection using single-channel EEG recording
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - Jalilifard, Amir
A1  - Pizzolato, Ednaldo Brigante
A1  - Jalilifard, Amir
A1  - Brigante Pizzolato, Ednaldo
A1  - Pizzolato, Ednaldo Brigante
A1  - Jalilifard, Amir
SP  - 820
EP  - 824
VL  - 2016
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2016.7590827
ID  - ref1
ER  -