Drowsiness detection for single channel EEG by DWT best m-term approximation
Silveira, Tiago da; Kozakevicius, Alice de Jesus; Rodrigues, Cesar Ramos
http://dx.doi.org/10.1590/2446-4740.0693
Res. Biomed. Eng., vol.31, n2, p.107-115, 2015
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Views: 894
Abstract
Introduction: In this paper we propose a promising new technique for drowsiness detection. It consists of applying the best m-term approximation on a single-channel electroencephalography (EEG) signal preprocessed through a discrete wavelet transform. Methods: In order to classify EEG epochs as awake or drowsy states, the most significant m terms from the wavelet expansion of an EEG signal are selected according to the magnitude of their coefficients related to the alpha and beta rhythms. Results: By using a simple thresholding strategy it provides hit rates comparable to those using more complex techniques. It was tested on a set of 6 hours and 50 minutes EEG drowsiness signals from PhysioNet Sleep Database yielding an overall sensitivity (TPR) of 84.98% and 98.65% of precision (PPV). Conclusion: The method has proved itself efficient at separating data from different brain rhythms, thus alleviating the requirement for complex post-processing classification algorithms.
Keywords
Signal processing, Drowsiness detection, Wavelet transform, Best m-term approximation, Frequency bands, DB2 Wavelet.
References
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Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989; 11(7):674-93. http://dx.doi.org/10.1109/34.192463
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Misiti M, Oppenheim G, Poggi JM. Wavelet toolbox 4 user's guide. Natick: Mathworks; 2010
Nielsen OM. Wavelets in scientific computing. Lyngby: Technical University of Denmark; 1998
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Papadelis C, Chen Z, Kourtidou-Papadeli C, Bamidis PD, Chouvarda I, Bekiaris E, Maglaveras N. Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology. 2007; 118(9):1906-22. http://dx.doi.org/10.1016/j.clinph.2007.04.031. PMid:17652020
Peden M, Scurfield L, Sleet D, Mohan D, Hyder AA, Jarawan E, Mathers C. World report on road traffic injury prevention. Geneva: World Health Organization; 2004. Available from: http://whqlibdoc.who.int/publications/2004/ 9241562609.pdf
Rosso OA, Blanco S, Yordanova J, Kolev V, Figliola A, Schürmann M, Başar E. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of Neuroscience Methods. 2001; 105(1):65-75. http://dx.doi.org/10.1016/S0165-0270(00)00356-3. PMid:11166367
Silveira, T. Drowsiness detection from a single electroencephalography channel through discrete wavelet transform [dissertation]. Santa Maria: Federal University of Santa Maria; 2012
Sinha RK. Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. Journal of Medical Systems. 2008; 32(4):291-9. http://dx.doi.org/10.1007/s10916-008-9134-z. PMid:18619093
Stollnitz EJ, Derose TD, Salesin DH. Wavelets for computer graphics: a primer, part 1. IEEE Computer Graphics and Applications. 1995; 15(3):76-84. http://dx.doi.org/10.1109/38.376616
Strang G, Nguyen T. Wavelets and filter banks. Wellesley: Wellesley College; 1996
Subasi A. Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients. Expert Systems with Applications. 2005; 28(4):701-11. http://dx.doi.org/10.1016/j.eswa.2004.12.027
Tan L, Kothapalli S, Chen L, Hussaini O, Bissiri R, Chen Z. A survey of power and energy efficient techniques for high performance numerical linear algebra operations. Parallel Computing. 2014; 40(10):559-73. http://dx.doi.org/10.1016/j.parco.2014.09.001
Temlyakov VN. Nonlinear methods of approximation. Foundations of Computational Mathematics. 2003; 3(1):33-107. http://dx.doi.org/10.1007/s102080010029
UniNova. Sleep electroencephalography signal database. Lisbon: UNINOVA; 2010
Yeo MV, Li X, Wilder-Smith EP. Characteristic EEG differences between voluntary recumbent sleep onset in bed and involuntary sleep onset in a driving simulator. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology. 2007; 118(6):1315-23. http://dx.doi.org/10.1016/j.clinph.2007.02.001. PMid:17398150
Avidan AY, Barkoukis TJ. Review of sleep medicine: expert consult. 3rd ed. St. Louis: Saunders; 2011
Bacchieri G, Barros AJD. Traffic accidents in Brazil from 1998 to 2010: many changes and few effects. Revista de Saude Publica. 2011; 45(5):949-63. http://dx.doi.org/10.1590/S0034-89102011005000069. PMid:21953026
Blinowska K, Durka P. Electroencephalography (EEG). New York: John Wiley; 2006
Beylkin G, Coifman RRV, Rokhlin V. Fast wavelet transforms and numerical algorithms I. Communications on Pure and Applied Mathematics. 1991; 44(2):141-83. http://dx.doi.org/10.1002/cpa.3160440202
Correa AG, Leber EL. An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of EEG records. In: Annual International Conference of the IEEE; 2010 Aug 31-Sept 4; Buenos Aires, Argentina. Piscataway: IEEE; 2010. p. 1405-8. http://dx.doi.org/10.1109/IEMBS.2010.5626721
Daubechies I. Ten lectures on wavelets. Philadelphia: SIAM; 1992
De Carli F, Nobili L, Gelcich P, Ferrillo F. A method for the automatic detection of arousals during sleep. Sleep. 1999; 22(5):561-72. PMid:10450591
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000; 101(23):E215-20. http://dx.doi.org/10.1161/01.CIR.101.23.e215. PMid:10851218
Hsu Y-L, Yang Y-T, Wang J-S, Hsu C-Y. Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing. 2013; 104:105-14. http://dx.doi.org/10.1016/j.neucom.2012.11.003
Hu S, Peters B, Zheng G. Driver fatigue detection from electroencephalogram spectrum after electrooculography artifact removal. IET Intelligent Transport Systems. 2013; 7(1):105-13. http://dx.doi.org/10.1049/iet-its.2012.0045
Hunn BP. The use of EEG as a workload assessment tool in flight test. California: Air Force Flight Test Center; 1993. Available from: http://www.dtic.mil/dtic/tr/fulltext/u2/a274568.pdf
Lal SKL, Craig A. Electroencephalography activity associated with driver fatigue: implications for a fatigue countermeasure device. Journal of Psychophysiology. 2001; 15(3):183-9. http://dx.doi.org/10.1027//0269-8803.15.3.183
Lawhern V, Kerick S, Robbins KA. Detecting alpha spindle events in EEG time series using adaptive autoregressive models. BMC Neuroscience. 2013; 14(101):1. http://dx.doi.org/10.1186/1471-2202-14-101. PMid:24047117
Liang S-F, Lin C-T, Wu R-C, Chen YC, Huang TY, Jung T-P. Monitoring driver's alertness based on the driving performance estimation and the EEG power spectrum analysis. In: Annual International Conference of the IEEE; 2006 Jan 17-18; Shangai, China. Piscataway: IEEE; 2006. p. 5738-41. http://dx.doi.org/ 10.1109/IEMBS.2005.1615791
Lin C-T. Wu R-C, Liang S-F, Chao W-H, Chen Y-J, Jung T-P. EEG-based drowsiness estimation for safety driving using independent component analysis. IEEE Transactions on Circuits and Systems. 2005; 52(12):2726-38. http://dx.doi.org/10.1109/TCSI.2005.857555
Lin C-T, Chang C-J, Lin B-S, Hung S-H, Chao C-F, Wang I-J. A real-time wireless brain-computer interface system for drowsiness detection. IEEE Biomedical Circuits and Systems. 2010; 4(4):214-22. http://dx.doi.org/10.1109/TBCAS.2010.2046415. PMid:23853367
Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989; 11(7):674-93. http://dx.doi.org/10.1109/34.192463
McKeown MJ, Humphries C, Achermann P, Borbély AA, Sejnowski TJ. A new method for detecting state changes in the EEG: exploratory application to sleep data. Journal of Sleep Research. 1998; 7(Suppl 1):48-56. http://dx.doi.org/10.1046/j.1365-2869.7.s1.8.x. PMid:9682194
Misiti M, Oppenheim G, Poggi JM. Wavelet toolbox 4 user's guide. Natick: Mathworks; 2010
Nielsen OM. Wavelets in scientific computing. Lyngby: Technical University of Denmark; 1998
Papadelis C, Kourtidou-Papadeli C, Bamidis PD, Chouvarda I, Koufogiannis D, Bekiaris E, Maglaveras N. Indicators of sleepiness in an ambulatory EEG study of night driving. In: 28th Annual International Conference of the IEEE; 2006 Aug 30-Sep 03; New York, USA. Piscataway: IEEE; 2006. p. 5738-41. http://dx.doi.org/ 10.1109/IEMBS.2006.259614
Papadelis C, Chen Z, Kourtidou-Papadeli C, Bamidis PD, Chouvarda I, Bekiaris E, Maglaveras N. Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology. 2007; 118(9):1906-22. http://dx.doi.org/10.1016/j.clinph.2007.04.031. PMid:17652020
Peden M, Scurfield L, Sleet D, Mohan D, Hyder AA, Jarawan E, Mathers C. World report on road traffic injury prevention. Geneva: World Health Organization; 2004. Available from: http://whqlibdoc.who.int/publications/2004/ 9241562609.pdf
Rosso OA, Blanco S, Yordanova J, Kolev V, Figliola A, Schürmann M, Başar E. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of Neuroscience Methods. 2001; 105(1):65-75. http://dx.doi.org/10.1016/S0165-0270(00)00356-3. PMid:11166367
Silveira, T. Drowsiness detection from a single electroencephalography channel through discrete wavelet transform [dissertation]. Santa Maria: Federal University of Santa Maria; 2012
Sinha RK. Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. Journal of Medical Systems. 2008; 32(4):291-9. http://dx.doi.org/10.1007/s10916-008-9134-z. PMid:18619093
Stollnitz EJ, Derose TD, Salesin DH. Wavelets for computer graphics: a primer, part 1. IEEE Computer Graphics and Applications. 1995; 15(3):76-84. http://dx.doi.org/10.1109/38.376616
Strang G, Nguyen T. Wavelets and filter banks. Wellesley: Wellesley College; 1996
Subasi A. Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients. Expert Systems with Applications. 2005; 28(4):701-11. http://dx.doi.org/10.1016/j.eswa.2004.12.027
Tan L, Kothapalli S, Chen L, Hussaini O, Bissiri R, Chen Z. A survey of power and energy efficient techniques for high performance numerical linear algebra operations. Parallel Computing. 2014; 40(10):559-73. http://dx.doi.org/10.1016/j.parco.2014.09.001
Temlyakov VN. Nonlinear methods of approximation. Foundations of Computational Mathematics. 2003; 3(1):33-107. http://dx.doi.org/10.1007/s102080010029
UniNova. Sleep electroencephalography signal database. Lisbon: UNINOVA; 2010
Yeo MV, Li X, Wilder-Smith EP. Characteristic EEG differences between voluntary recumbent sleep onset in bed and involuntary sleep onset in a driving simulator. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology. 2007; 118(6):1315-23. http://dx.doi.org/10.1016/j.clinph.2007.02.001. PMid:17398150
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