Sleep-spindle identification on EEG signals from polysomnographie recordings using correntropy

Sleep spindles (SSs) are characteristic electroencephalographic (EEG) waveforms of sleep stages N2 and N3. One of the main problems associated with SS detection is the high number of false positives. In this paper we propose a new periodogram based on correntropy to detect SSs and enhance their characterization. Correntropy is a generalized correlation, under the information theoretic learning framework. A non-negative matrix factorization decomposition of correntropy allows us to obtain a new periodogram, which shows an improved resolution capability compared to the conventional power spectrum density. Preliminary results show that the proposed method obtained a sensitivity rate of 0.868 with a false positive rate of 0.121.

[1]  J. R. Smith,et al.  Sleep spindle characteristics as a function of age. , 1982, Sleep.

[2]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[3]  Ata Akin,et al.  Detection of sleep spindles by discrete wavelet transform , 1998, Proceedings of the IEEE 24th Annual Northeast Bioengineering Conference (Cat. No.98CH36210).

[4]  Mingui Sun,et al.  Characterization of sleep spindles using higher order statistics and spectra , 2000, IEEE Transactions on Biomedical Engineering.

[5]  G. Lawton.  Why do we sleep? , 2000, Nature Neuroscience.

[6]  Kemal Leblebicioglu,et al.  Sleep spindles detection using short time Fourier transform and neural networks , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[7]  M. Ferrara,et al.  Sleep spindles: an overview. , 2003, Sleep medicine reviews.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  中村 元昭.  Sleep spindles in human prefrontal cortex : an electrocorticographic study , 2004 .

[10]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[11]  Dongxu Qi,et al.  Detection of Spindles in Sleep EEGs Using a Novel Algorithm Based on the Hilbert-Huang Transform , 2006 .

[12]  Günther J. L. Gerhardt,et al.  Benchmarking matching pursuit to find sleep spindles , 2006, Journal of Neuroscience Methods.

[13]  Neil Genzlinger.  A. and Q , 2006 .

[14]  M. J. Spinosa,et al.  Sleep spindles: validated concepts and breakthroughs , 2007 .

[15]  H. Scholle,et al.  Sleep spindle evolution from infancy to adolescence , 2007, Clinical Neurophysiology.

[16]  C. Held,et al.  Sleep Spindle Detection by Using Merge Neural Gas , 2007 .

[17]  S. Himanen,et al.  Diffuse sleep spindles show similar frequency in central and frontopolar positions , 2008, Journal of Neuroscience Methods.

[18]  Jose C. Principe,et al.  Information Theoretic Learning - Renyi's Entropy and Kernel Perspectives , 2010, Information Theoretic Learning.

[19]  F. Oort,et al.  Chronic sleep reduction in adolescents , 2012 .

[20]  Claudio A. Perez,et al.  Automated Sleep-Spindle Detection in Healthy Children Polysomnograms , 2010, IEEE Transactions on Biomedical Engineering.

[21]  Pavlos Protopapas,et al.  An Information Theoretic Algorithm for Finding Periodicities in Stellar Light Curves , 2012, IEEE Transactions on Signal Processing.

[22]  W. Marsden.  I and J , 2012 .

[23]  Pietro Perona,et al.  Sleep spindle detection: crowdsourcing and evaluating performance of experts, non-experts, and automated methods , 2014, Nature Methods.