Automated drowsiness detection through wavelet packet analysis of a single EEG channel

Ratio indices computed from a single EEG channel used as drowsiness indicators.Delta and gamma brain rhythms successfully used for drowsiness detection.Wavelet packet transform as the main tool to localize specific brain frequency ranges.Transition from alert to drowsy state is taken as main event of interest.Wilcoxon signed rank test analysis pointed out the contribution of proposed indices. Advances in materials engineering, electronic circuits, sensors, signal processing and classification techniques have allowed computational systems to interpret biological quantities, recognizing physiological conditions. The next scientific challenge is to turn those technologies portable, wearable or even implantable, above all, being energy efficient. A prospective application for the next generation of portable electroencephalogram (EEG) signal processing systems is hazard prevention in attention-demanding activities. EEG keeps closest connection to the preoptic area where sleep is originated. In this paper, a methodology for assessing alertness level based on a single EEG channel (Pz-Oz) is proposed, allowing the reduction of the required hardware and the computational time of the algorithms, besides being more portable than multi-channel based ones. Two new spectral power-based indices (i) γ/? and (ii) ( γ + β )/( ? + α ) are computed from EEG rhythms through the normalized Haar discrete wavelet packet transform (WPT). The Haar WPT allows precisely resolving the brain rhythms into packets whilst demanding a relatively low computational cost. The effectiveness of the proposed indices in drowsiness detection is evaluated by comparison with five indices originally proposed for multi-channel processing. Statistical Wilcoxon signed rank test is applied to evaluate the performance of the entire set of indices, evidencing the significant changes in the alert-drowsy transitions of 20 subjects of a public database. The proposed indices (ii) and (i) presented the most and second more significant p-Values (p < 0.001 and p?=?0.001), respectively.

[1]  Koji Oguri,et al.  Estimation of drowsiness level based on eyelid closure and heart rate variability , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Zhiwei Zhu,et al.  Active facial tracking for fatigue detection , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[3]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients , 2005, Expert Syst. Appl..

[4]  Marina Ronzhina,et al.  Sleep scoring using artificial neural networks. , 2012, Sleep medicine reviews.

[5]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[6]  Luca Citi,et al.  Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses , 2014, Science Translational Medicine.

[7]  M. Chung,et al.  Electroencephalographic study of drowsiness in simulated driving with sleep deprivation , 2005 .

[8]  Yan Li,et al.  Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal , 2014, IEEE Journal of Biomedical and Health Informatics.

[9]  Natheer Khasawneh,et al.  Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier , 2012, Comput. Methods Programs Biomed..

[10]  R J Fairbanks,et al.  RESEARCH ON VEHICLE-BASED DRIVER STATUS/PERFORMANCE MONITORING; DEVELOPMENT, VALIDATION, AND REFINEMENT OF ALGORITHMS FOR DETECTION OF DRIVER DROWSINESS. FINAL REPORT , 1994 .

[11]  F. Ebrahimi,et al.  Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  A. Kozakevicius,et al.  Wavelet transform with special boundary treatment for 1D data , 2013 .

[13]  Aeilko H. Zwinderman,et al.  Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG , 2000, IEEE Transactions on Biomedical Engineering.

[14]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[15]  T. Sejnowski,et al.  Estimating alertness from the EEG power spectrum , 1997, IEEE Transactions on Biomedical Engineering.

[16]  J. Mattout,et al.  Automatic analysis of single-channel sleep EEG: validation in healthy individuals. , 2007, Sleep.

[17]  C Tarriere,et al.  AN ON-BOARD SYSTEM FOR DETECTING LAPSES OF ALERTNESS IN CAR DRIVING , 1995 .

[18]  E. J. Stollnitz,et al.  Wavelets for Computer Graphics : A Primer , 1994 .

[19]  Chin-Teng Lin,et al.  A Real-Time Wireless Brain–Computer Interface System for Drowsiness Detection , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[20]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[21]  R J Fairbanks,et al.  RESEARCH ON VEHICLE-BASED DRIVER STATUS/PERFORMANCE MONITORING: SEVENTH SEMI-ANNUAL RESEARCH REPORT , 1995 .

[22]  Aihua Zhang,et al.  Drowsiness detection based on wavelet analysis of ECG and pulse signals , 2012, 2012 5th International Conference on BioMedical Engineering and Informatics.

[23]  Syed Anas Imtiaz,et al.  A Low Computational Cost Algorithm for REM Sleep Detection Using Single Channel EEG , 2014, Annals of Biomedical Engineering.

[24]  A. Kohn,et al.  Gamma Rhythms in the Brain , 2011, PLoS biology.

[25]  Fabio Babiloni,et al.  Evaluation of the workload and drowsiness during car driving by using high resolution EEG activity and neurophysiologic indices , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Cesar Ramos Rodrigues,et al.  Drowsiness detection for single channel EEG by DWT best m-term approximation , 2015 .

[27]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[28]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level by using wavelet transform and artificial neural network , 2004, Journal of Neuroscience Methods.

[29]  Bruce P. Hunn.  The Use of EEG as a Workload Assessment Tool in Flight Test , 1993 .

[30]  Aini Hussain,et al.  Development of vehicle driver drowsiness detection system using electrooculogram (EOG) , 2005, 2005 1st International Conference on Computers, Communications, & Signal Processing with Special Track on Biomedical Engineering.

[31]  Masayoshi Kamijo,et al.  Relationship between Facial Expression and Facial Electromyogram (f-EMG) Analysis in the Expression of Drowsiness , 2011, 2011 International Conference on Biometrics and Kansei Engineering.

[32]  David Salesin,et al.  Wavelets for computer graphics: a primer.1 , 1995, IEEE Computer Graphics and Applications.

[33]  Gert Cauwenberghs,et al.  Wireless non-contact cardiac and neural monitoring , 2010, Wireless Health.

[34]  B. Kemp,et al.  Model-based monitoring of human sleep stages , 1987 .

[35]  Tzyy-Ping Jung,et al.  EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach , 2008, EURASIP J. Adv. Signal Process..

[36]  M. Corsi-Cabrera,et al.  Power and coherent oscillations distinguish REM sleep, stage 1 and wakefulness. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[37]  Marc Vidal,et al.  Interplay between BRCA1 and RHAMM Regulates Epithelial Apicobasal Polarization and May Influence Risk of Breast Cancer , 2011, PLoS biology.

[38]  Rajesh P. N. Rao,et al.  A Direct Brain-to-Brain Interface in Humans , 2014, PloS one.

[39]  Evangelos Bekiaris,et al.  Using EEG spectral components to assess algorithms for detecting fatigue , 2009, Expert Syst. Appl..

[40]  Gwan S. Choi,et al.  Support Vector Machine Based Detection of Drowsiness Using Minimum EEG Features , 2013, 2013 International Conference on Social Computing.

[41]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[42]  J. Allan Hobson,et al.  A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects: A. Rechtschaffen and A. Kales (Editors). (Public Health Service, U.S. Government Printing Office, Washington, D.C., 1968, 58 p., $4.00) , 1969 .

[43]  Mark L. Nagurka,et al.  Detecting slow wave sleep using a single EEG signal channel , 2015, Journal of Neuroscience Methods.

[44]  Ludo Verhoeven,et al.  Improvements in Spelling after QEEG-based Neurofeedback in Dyslexia: A Randomized Controlled Treatment Study , 2009, Applied psychophysiology and biofeedback.

[45]  Erin Kara,et al.  TOWARD EARLY-WARNING DETECTION OF GRAVITATIONAL WAVES FROM COMPACT BINARY COALESCENCE , 2011, 1107.2665.

[46]  Miguel A. Labrador,et al.  Centinela: A human activity recognition system based on acceleration and vital sign data , 2012, Pervasive Mob. Comput..