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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-45ef2186-9b71-484c-b5fe-0fb7dfe9b87a

Czasopismo

Przegląd Elektrotechniczny

Tytuł artykułu

Classification of Driver Drowsiness Level using Wireless EEG

Autorzy Wali, M. K.  Murugappan, M.  Badlishah-Ahmad, R. 
Treść / Zawartość http://pe.org.pl/
Warianty tytułu
PL Badania senności kierowcy na podstawie sygnału EEG
Języki publikacji EN
Abstrakty
EN In this work, wireless Electroencephalogram (EEG) signals are used to classify the driver drowsiness levels (neutral, drowsy, high drowsy and sleep stage1) based on Discrete Wavelet Packet Transform (DWPT). Two statistical features (spectral centroid, and power spectral density) were extracted from four EEG frequency bands (delta, theta, alpha, and beta) using Fast Fourier Transform (FFT). These features are used to classify the driver drowsiness level using three classifiers namely, subtractive fuzzy clustering, probabilistic neural network, and K nearest neighbour. Results of this study indicates that the best average accuracy of 84.41% is achieved using subtractive fuzzy classifier based on power spectral density feature extracted by db4 wavelet function.
PL W artykule zaprezentowano możliwość wykorzystania dyskretnej transformaty falkowej do analizy sygnału elektroencefalografii w badaniach senności kierowcy. Parametry statystyczne sygnału analizowano z wykorzystaniem dyskretnej transformaty Fouriera. Stwierdzono że najlepsza dokładność uzyskuje się stosując klasyfikator rozmyty i funkcję falkową db4.
Słowa kluczowe
PL dyskretna transformata falkowa   senność   dyskretna transformata Fouriera  
EN discrete wavelet transform   EEG   fast Fourier transform   fuzzy inference system  
Wydawca Wydawnictwo SIGMA-NOT
Czasopismo Przegląd Elektrotechniczny
Rocznik 2013
Tom R. 89, nr 6
Strony 113--117
Opis fizyczny Bibliogr. 15 poz., rys., tab., wykr.
Twórcy
autor Wali, M. K.
autor Murugappan, M.
autor Badlishah-Ahmad, R.
Bibliografia
[1] Lyznicki M.J., Doege C.T., Davis M.R., Williams A.W., “Sleepiness, driving, and motor vehicle crashes,” JAMA, 279(23):1908-1913, 1998.
[2] Horne A.J., Reyner A.L., “Sleep-related vehicle accidents,” British Medical Journal, 310:565-567, 1995.
[3] McCartt T.A., Ribner A.S., Pack A.I., Hammer C.M., “The scope and nature of the drowsy driving problem in New Youk State, “ Accident Analysis and Prevention, 28 (4):511-517, 1996.
[4] Hayami T. , Matsunaga K., Shidoji K., Matsuki Y., “Detecting Drowsiness While Driving by Measuring Eye Movement-A Pilot Study,” in Proc. 5th Internation Conference on Intelligent Transportation Systems, Singapore, 2002, pp. 156-161.
[5] HamadaT., Adachi K., Nakano T., Yamamoto S., “Detecting Method for Drivers’ Drowsiness Applicabel to Individual Features,” In Proc. 2003 Intelligent Transportation Systems, Vol. 2, 2003, pp 1405-1410.
[6] Tsai P., Weichih H., Kuo B.J., Shyu L., “A Portable Device for Real Time Drowsiness Detection Using Novel Active Dry Electrode System,” In Proc. 2009 International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, 2009, September 2-6.
[7] Subasi A., “Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients,”Expert Systems with Applications, 28, pp. 701–711, 2005.
[8] Torbjorn A., Peter M., Kecklund G., “ Sleep and Recovery” Current Perspectives on Job-Stress Recovery Research in Occupational Stress and Well Being, by Emerald Group Volume 7, 2009, 205–247.
[9] Carskadon, MA, Dement WC, Monitoring and staging human sleep. In M.H. Kryger, T. Roth, & W.C. Dement (Eds.), Principles and practice of sleep medicine, 5th edition, (pp 16- 26). St. Louis: Elsevier Saunders, 2011.
[10] Murugappan M., Nagarajan R., Yaacob S., “Comparison of Different Wavelet Features fromEEG Signals for Classifying Human Emotions”, IEEE Symposium on Industrial Electronics and Applications Kuala Lumpur, Malaysia, October 4-6, 2009.
[11] Acharya U.R., Oliver F., VinithaSree S., Chuan A., ”Atheromatic Symptomatic vs. Asymptomatic Classification Of Carotid Ultrasound Plaque using a combination of HOS, DWT &Texture”,EMBS Boston Conference 2011
[12] Wali K.M., Murugappan M., Badlishah A., Zheng B.,”Development of Discrete Wavelet Transform (DWT) Toolbox for Signal Processing Applications”,2012 International Conference on Biomedical Engineering (ICoBE),,Penang, Malaysia,27-28 February 2012.
[13] Glassman, E. L., "A wavelet-like filter based on neuron action potentials for analysis of human scalp electroencephalographs." Biomedical Engineering, IEEE Transactions on, 52(11), 1851-1862. 2005.
[14] Rizon M., Murugappan M., Nagarajan R., Yaacob S.,”Asymmetric Ratio and FCM based Salient Channel Selection for Human Emotion Detection Using EEG”,WSEASTransactions on signal processing, Issue 10, Volume 4, October 2008.
[15] HEKIM M.,” ANN-based classification of EEG signals using the average power based on rectangle approximation window”, Electrical Review, 88, 2012.
Kolekcja BazTech
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