The Application of Visual Organization Principle in the Detection of Sleep Spindles
- 1 East China University of Science and Technology, Shanghai, China
- 2 East China University of Science and Technology, Shanghai, China
- 3 East China University of Science and Technology, Shanghai, China
Abstract
In order to detect the sleep spindles simply and efficiently, a novel time-domain approach to detect sleep spindles based on the principles of visual organization is proposed. The code idea of the visual organization is to organize the primary visual elements according to some rules of organization, and to form a more meaningful object of visual processing, as the input of next process. After the collected EEG is processed with the merging algorithm based on the principle of visual organization, it can extract the time-domain feature frequency and duration time better. Use these features with a simple algorithm to detect spindles achieving sensitivity of 92.5% and specificity of 98.1%, which verifies the validity of this method to detect the sleep spindles.
- De, G.L. and Errara, M.F. (2003) Sleep Spindles: An Overview. Sleep Medicine Reviews, 7, 423-440. http://dx.doi.org/10.1053/smrv.2002.0252.
- Vu, D.T.T., Mckinney, S.M, Buxton, O.M., et al. (2010) Spontaneous Brain Rhythms Predict Sleep Stability in the Face of Noise. Current Biology Cb, 20, 626-627. http://dx.doi.org/10.1016/j.cub.2010.06.032
- Fogel, S.M. and Smith, C.T. (2010) The function of the Sleep Spindle: A Physio-logical Index of Intelligence and a Mechanism for Sleep-Dependent Memory Consolidation. Neuroscience and Biobehavioral Reviews, 35, 1154-1165. http://dx.doi.org/10.1016/j.neubiorev.2010.12.003
- Forest, G., Poulin, J., Daoust, A.M., etal. (2007) Attention and non-REM sleep in Neuroleptic-Naive Persons with Schizophrenia and Control Participants. Psychiatry Research, 149, 33-40. http://dx.doi.org/10.1016/j.psychres.2005.11.005
- Campbell, K. and Hofman W. (1980) Human and Automatic Validation of a Phase-Locked Loop Spindle Detection System. Electroencephalography & Clinical Neurophysiolgy, 48, 671-674. http://dx.doi.org/10.1016/0013-4694(80)90296-5
- Gorur, D., Halici, U., Aydin, H., et al. (2002) Sleep Spindles Detection Using Short Time Fourier Transform and Neural Networks. Neural Networks, IJCNN '02. Proceedings of the 2002 International Joint Conference on. IEEE, 1631-1636. http://dx.doi.org/10.1109/IJCNN.2002.1007762
- Nonclercq, A., Urbain, C., Verheulpen, D., etal. (2013) Sleep Spindle Detection through Amplitude–Frequency Normal Modelling. Journal of Neuroscience Methods, 214, 192-203. http://dx.doi.org/10.1016/j.jneumeth.2013.01.015
- Wendt, S.L., Christensen, J., Kempfner, J., etal. (2012) Validation of a Novel Automatic Sleep Spindle Detector with High Performance during Sleep in Middle Aged Subjects. International Conference of the IEEE Engineering in Medicine & Biology Society. Conference Proceedings IEEE EngMedBiolSoc, 4250-4253. http://dx.doi.org/10.1109/EMBC.2012.6346905
- Luo, S.W. (2006) Visual Perception System Information Processing Theory. Beijing: Electronics Industry Press.
- Ben-Av, M.B. and Sagi, D. (1995) Perceptual Grouping by Similarity and Proximity: Expe-rimental Results Can Be Predicted by Intensity Autocorrelations. Vision Research, 35, 853-866. http://dx.doi.org/10.1016/0042-6989(94)00173-J.
- Mack, A., Tang, B., Tuma, R., etal. (1992) Perceptual organization and atten-tion..Cognitive Psychology, 24, 475-501. http://dx.doi.org/10.1016/0010-0285(92)90016-U