The ANN-based computing of drowsy level

Authors:
Muhammed B. Kurt;Necmettin Sezgin;Mehmet Akin;Gokhan Kirbas;Muhittin Bayram
Affiliations:
Department of Electrical and Electronics Engineering, University of Dicle, 21280 Diyarbakir, Turkey;Department of Electrical and Electronics Engineering, University of Dicle, 21280 Diyarbakir, Turkey;Department of Electrical and Electronics Engineering, University of Dicle, 21280 Diyarbakir, Turkey;Faculty of Medicine, University of Dicle, Diyarbakir, Turkey;Department of Electrical and Electronics Engineering, University of Dicle, 21280 Diyarbakir, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 3
Cited 5

Classification of EEG signals using the wavelet transform

Signal Processing
Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals

Journal of Medical Systems
Automatic recognition of vigilance state by using a wavelet-based artificial neural network

Neural Computing and Applications

EOG-based Human-Computer Interface system development

Expert Systems with Applications: An International Journal
Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: a neural network approach

Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Feature extraction of forearm EMG signals for prosthetics

Expert Systems with Applications: An International Journal
Wavelet basis functions in biomedical signal processing

Expert Systems with Applications: An International Journal
Analysis of alertness status of subjects undergoing the cortical auditory evoked potential hearing test

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I

Quantified Score

Hi-index	12.06

Visualization

Abstract

We have developed a new method for automatic estimation of vigilance level by using electroencephalogram (EEG), electromyogram (EMG) and eye movement (EOG) signals recorded during transition from wakefulness to sleep. In the previous studies, EEG signals and EEG signals with EMG signals were used for estimating vigilance levels. In the present study, it was aimed to estimate vigilance levels by using EEG, EMG and EOG signals. The changes in EEG, EMG and EOG were diagnosed while transiting from wakefulness to sleep by using wavelet transform and developed artificial neural network (ANN). EEG signals were separated to its subbands using wavelet transform, LEOG (Left EOG), REOG (Right EOG) and chin EMG was used in ANN process for increasing the accuracy of the estimation rate by evaluating their tonic levels and also used in data preparation stage to verify and eliminate the movement artifacts. Then, training and testing data sets consist of the EEG subbands (delta, theta, alpha and beta); LEOG, REOG and EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: Awake, drowsy, and sleep. The accuracy of estimation is about 97-98% while the accuracy of the previous study which used only EEG was 95-96% and the study which used EEG with EMG was 98-99%. The reason of decreasing the percentage of present study according to the last study is because of the increase of the input data.