Optimization-based features extraction for K-complex detection

Abstract

The K-complex is a transient electroencephalogram (EEG, brain activity) waveform that contributes to sleep stage scoring. An automated detection of K-complexes is an important component of sleep stage monitoring. This automation is difficult due to the stochastic nature of brain signals, presence of noise, complexity, and extreme size of data. We develop an optimization model, based on solving a sequence of linear least squares problems, to extract key features of EEG signals. The proposed approach significantly reduces the dimension of the problem and the computational time while the classification accuracy is enhanced in most cases. Numerical results show that this procedure is efficient in detecting K-complexes. References R. Agarwal and J. Gotman. Digital tools in polysomnography. J. Clin. Neurophysiol., 19(2):136–143, 2002. http://journals.lww.com/clinicalneurophys/Abstract/2002/03000/Digital_Tools_in_Polysomnography.4.aspx . J. L. Barlow. Numerical aspects of solving linear least squares problems. Technical report, Computer Science Department, The Pennsylvania State University, University Park, PA, USA, January 1999. www.cse.psu.edu/ barlow/book.ps . A. Bjorck. Numerical Methods for Least Squares Problems. Handbook of Numerical Analysis. SIAM, 1996. doi:10.1137/1.9781611971484 . S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2010. http://www.cambridge.org/au/academic/subjects/statistics-probability/optimization-or-and-risk/convex-optimization?format=HB . G. Bremer, J. R. Smith, and I. Karacan. Automatic detection of the K-complex in sleep electroencephalograms. IEEE T. Bio-Med. Eng., 17(4):314–323, 1970. doi:10.1109/TBME.1970.4502759 . P. R. Carney, R. B. Berry, and J. D. Geyer. Clinical Sleep Disorders. LWW medical book collection. Lippincott Williams and Wilkins, 2005. http://www.lww.com/Product/9780781786928 . G. H. Golub and C. F. Van Loan. Matrix Computations. Johns Hopkins Studies in the Mathematical Sciences. Johns Hopkins University Press, Baltimore, MD, USA, 1996. http://portal.acm.org/citation.cfm?id=248979 . C. Iber, S. Ancoli-Israel, A. L. Chesson, and S. F. Quani. AASM manual for the scoring of sleep and associated events. Rules, technology and technical specifications. Technical report, AASM, Westchester, IL, 2007. http://www.aasmnet.org/scoringmanual/v2.0.2/html/index.html?IXDevelopmentProcess.html . B. H. Jansen. Artificial neural nets for K-complex detection. IEEE Eng. Med. Biol., 9(3):50–52, 1990. doi:10.1109/51.59213 . A. Kales, A. Rechtschaffen, Los Angeles University of California, and NINDB Neurological Information Network (U.S.). A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. U. S. National Institute of Neurological Diseases and Blindness, Neurological Information Network Bethesda, Md, 1968. http://nla.gov.au/nla.cat-vn823711 . J. E. Mitchell. Branch-and-cut algorithms for combinatorial optimization problems. In P. M. Pardalos and M. G. C. Resende, editors, Handbook of Applied Optimization, pages 65–77. Oxford University Press, 2002. http://global.oup.com/academic/product/handbook-of-applied-optimization-9780195125948?cc=au&lang=en& . D. Moloney, N. Sukhorukova, P. Vamplew, J. Ugon, G. Li, G. Beliakov, C. Philippe, H. Amiel, and A. Ugon. Detecting K-complexes for sleep stage identification using nonsmooth optimization. ANZIAM J., 52:319–332, 2011. doi:10.1017/S1446181112000016 . O. Sheriff, B. Pagnrek, S. Mamouhd, and R. Broughton. Automatic detection of K-complex in sleep EEG. Int. Electrical Electronic Conf. Exp., 81, 1977. Z. Tang and N. Ishii. Detection of the K-complex using a new method of recognizing waveform based on the discrete wavelet transform. IEICE T. Inf. Syst., E78–D(1):77–85, 1995. http://search.ieice.org/bin/summary.php?id=e78-d_1_77 . L. N. Trefethen and D. Bau. Numerical Linear Algebra. SIAM, 1997. http://bookstore.siam.org/ot50/ . Weka web site. www.cs.waikato.ac.nz/ml/weka/ .