Study on the property of correlation dimension of sleep apnea syndrome electroencephalogram_Journal of Biomedical Engineering

Authors：

 ZHOU Jing , WU Xiaoming

Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, P.R.China;

Corresponding author：

ZHOU Jing, Email: hellozj@scut.edu.cn

Keywords：

sleep apnea syndrome; electroencephalogram; nonlinearity; correlation dimension

DOI：

10.7507/1001-5515.201604045

Video：

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Sleep apnea syndrome (SAS) is a kind of common and harmful systemic sleep disorder. SAS patients have significant iconography changes in brain structure and function, and electroencephalogram (EEG) is the most intuitive parameter to describe the sleep process which can reflect the electrical activity and function of brain tissues. Based on the non-stationary and nonlinear characteristics of EEG, this paper analyzes the correlation dimension of sleep EEG in patients with SAS. Six SAS patients were classed as SAS group and six healthy persons were classified into a control group. The results showed that the correlation dimension of sleep EEG in the SAS group and the control group decreased gradually with the deepening of sleep, and then increased to the level of awake and light sleep stage with rapid eye movement (REM). The correlation dimension of SAS group was significantly lower than that of control group (P<0.01) throughout all the stages. The results suggested that there were significant nonlinear dynamic differences between the EEG signals of SAS patients and of healthy people, which provided a new direction for the study of the physiological mechanism and automatic detection of SAS.

Citation： ZHOU Jing, WU Xiaoming. Study on the property of correlation dimension of sleep apnea syndrome electroencephalogram. Journal of Biomedical Engineering, 2017, 34(2): 168-172. doi: 10.7507/1001-5515.201604045 Copy

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2.	王永杰. 打鼾不治会得大病. 大众卫生报. 2005.5.10.
3.	Young T, Peppard P, Palta M, et al. Population-based study of sleep-disordered breathing as a risk factor for hypertension. Arch Intern Med, 1997, 157(15): 1746-1752.
4.	Dimsdale J E, Loredo J S, Profant J. Effect of continuous airway pressure on blood pressure. Hypertension, 2000, 35: 144-147.
5.	于兰, 贾占玲. 睡眠与睡眠障碍, 长春, 吉林人民出版社: 2006.
6.	Zimmerman M E, Aloia M S. A review of neuroimaging in obstructive sleep apnea. J Clin Sleep Med, 2006, 2(4): 461-471.
7.	Ayalon L, Peterson S. Functional central nervous system imaging in the investigation of obstructive sleep apnea. Curr Opin Pulm Med, 2007, 13(6): 479-483.
8.	Nowak M, Kornhuber J, Meyrer R. Daytime impairment and neurodegeneration in OSAS. Sleep, 2006, 29(12): 1521-1530.
9.	Malinowska U, Durka P J, Blinowska K J, et al. Micro- and macrostructure of sleep EEG. IEEE Eng Med Biol Mag, 2006, 25(4): 26-31.
10.	Peiris M T R, Jones R D, Davidson P R. Detecting behavioral microsleeps from EEG power spectra// In Proceedings of the 28th IEEE international conference on engineering in medicine and biology society, 2006: 5723-5726.
11.	Sugi T, Nakamura M, Shimokawa T, et al. Automatic detection of EEG arousals by use of normalized parameters for different subjects//In Proceedings of the IEEE Asian-Pacific conference on engineering in medicine and biology society, 2003: 146-147.
12.	Ventouras E M, Ktonas P Y, Tsekou H, et al. Slow and fast EEG sleep spindle component extraction using Independent component analysis//In Proceedings of the 8th IEEE international conference on bioinformatics and biomedical engineering, 2008: 1-6.
13.	Zhang D X, Wu X P, Guo X J. The EEG signal preprocessing based on empirical mode decomposition//In Proceedings of the 2nd IEEE international conference on bioinformatics and biomedical engineering, 2008: 2131-2134.
14.	Kantz H, Sehreiber T. Nonlinear time series analysis. Cambridge: cambridge university press, 1996.
15.	Pritchard W S, Duke D W, Krieble K K. Dimensional analysis of resting human EEG. II: Surrogate-data testing indicates nonlinearity but not low-dimensional chaos. Psychophysiology, 1995, 32(5): 486-491.
16.	Babloyantz A, Salazar J M, Nicolis C. Evidence of chaotic dynamics of brain activity during the sleep cycle. Phys Lett A, 1985, 111(3): 152-156.
17.	Babloyantz A, Destexhe A. Low-dimensional chaos in an instance of epilepsy. Proc Natl Acad Sci U S A, 1986, 83(10): 3513-3517.
18.	Pijin J P, Neerven J V, Noest A, et al. Chaos or noise in EEG signals. dependence on state and brain site. Electroencephalogr Clin Neurophysiol, 1991, 79: 371-381.
19.	Stam C J, Jelles B, Achtereekte H A, et al. Investigation of EEG non-linearity in dementia and Parkinson's disease. Electroencephalogr Clin Neurophysiol, 1995, 95(5): 309-317.
20.	Stam C J, Jelles B, Achtereekte H A, et al. Diagnostic usefulness of linear and nonlinear quantitative EEG analysis in Alzheimer's disease. Clin Electroencephalogr, 1996, 27(2): 69-77.
21.	Takens F. Detecting strange attractors in turbulence. Lecture Notes in Mathematics, 1981, 898(1): 366-381.
22.	李红利.癫痫脑电信号的非线性分析. 天津: 天津大学, 2012.
23.	CAO Liangyue. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 1997, 110(1/2): 43-50.
24.	Grassberger J, Procacria I. Measuring the strangeness of strange attractor. Physica D, 1983, 9: 189-208.
25.	Janjarasjitt S, Scher M S, Loparo K A. Nonlinear dynamical analysis of the neonatal EEG time series: the relationship between sleep state and complexity. Clin Neurophysiol, 2008, 119(8): 1812-1823.
26.	丁黎明.睡眠脑电的混沌学研究. 镇江: 江苏大学, 2007.
27.	Coito A S, Sanches J. Assessment of obstructive sleep apnea syndrome by spectral analysis of physiological parameters//1st Portuguese Meeting in Bioengineering, 2011: 2.
28.	Coito A S, Belo D, Paiya T, et al. Topographic EEG brain mapping before, during and after Obstructive Sleep Apnea Episodes// 2011 IEEE International Symposium on/ Biomedical Imaging: From Nano to Macro, 2011: 1860-1863.
29.	Akṣahin M, Aydın S, Fırat H, et al. Artificial apnea classification with quantitative sleep EEG synchronization. J Med Syst, 2012, 36(1): 139-144.
30.	Almuhammadi W S, Aboalayon K A, Faezipour M. Efficient obstructive sleep apnea classification based on EEG signals//2015 IEEE LONG ISLAND SYSTEMS, APPLICATIONS AND TECHNOLOGY CONFERENCE (LISAT), 2015: 1-6.
31.	江朝晖, 冯焕清, 刘大路, 等. 改进 G-P 算法与睡眠脑电的关联维. 北京生物医学工程, 2004, 23(4): 260-262.

1. Guilleminault C, Tilkian A, Dement W C. The sleep apnea syndrome. Annu Rev Med, 1976, 27: 465-484.
2. 王永杰. 打鼾不治会得大病. 大众卫生报. 2005.5.10.
3. Young T, Peppard P, Palta M, et al. Population-based study of sleep-disordered breathing as a risk factor for hypertension. Arch Intern Med, 1997, 157(15): 1746-1752.
4. Dimsdale J E, Loredo J S, Profant J. Effect of continuous airway pressure on blood pressure. Hypertension, 2000, 35: 144-147.
5. 于兰, 贾占玲. 睡眠与睡眠障碍, 长春, 吉林人民出版社: 2006.
6. Zimmerman M E, Aloia M S. A review of neuroimaging in obstructive sleep apnea. J Clin Sleep Med, 2006, 2(4): 461-471.
7. Ayalon L, Peterson S. Functional central nervous system imaging in the investigation of obstructive sleep apnea. Curr Opin Pulm Med, 2007, 13(6): 479-483.
8. Nowak M, Kornhuber J, Meyrer R. Daytime impairment and neurodegeneration in OSAS. Sleep, 2006, 29(12): 1521-1530.
9. Malinowska U, Durka P J, Blinowska K J, et al. Micro- and macrostructure of sleep EEG. IEEE Eng Med Biol Mag, 2006, 25(4): 26-31.
10. Peiris M T R, Jones R D, Davidson P R. Detecting behavioral microsleeps from EEG power spectra// In Proceedings of the 28th IEEE international conference on engineering in medicine and biology society, 2006: 5723-5726.
11. Sugi T, Nakamura M, Shimokawa T, et al. Automatic detection of EEG arousals by use of normalized parameters for different subjects//In Proceedings of the IEEE Asian-Pacific conference on engineering in medicine and biology society, 2003: 146-147.
12. Ventouras E M, Ktonas P Y, Tsekou H, et al. Slow and fast EEG sleep spindle component extraction using Independent component analysis//In Proceedings of the 8th IEEE international conference on bioinformatics and biomedical engineering, 2008: 1-6.
13. Zhang D X, Wu X P, Guo X J. The EEG signal preprocessing based on empirical mode decomposition//In Proceedings of the 2nd IEEE international conference on bioinformatics and biomedical engineering, 2008: 2131-2134.
14. Kantz H, Sehreiber T. Nonlinear time series analysis. Cambridge: cambridge university press, 1996.
15. Pritchard W S, Duke D W, Krieble K K. Dimensional analysis of resting human EEG. II: Surrogate-data testing indicates nonlinearity but not low-dimensional chaos. Psychophysiology, 1995, 32(5): 486-491.
16. Babloyantz A, Salazar J M, Nicolis C. Evidence of chaotic dynamics of brain activity during the sleep cycle. Phys Lett A, 1985, 111(3): 152-156.
17. Babloyantz A, Destexhe A. Low-dimensional chaos in an instance of epilepsy. Proc Natl Acad Sci U S A, 1986, 83(10): 3513-3517.
18. Pijin J P, Neerven J V, Noest A, et al. Chaos or noise in EEG signals. dependence on state and brain site. Electroencephalogr Clin Neurophysiol, 1991, 79: 371-381.
19. Stam C J, Jelles B, Achtereekte H A, et al. Investigation of EEG non-linearity in dementia and Parkinson's disease. Electroencephalogr Clin Neurophysiol, 1995, 95(5): 309-317.
20. Stam C J, Jelles B, Achtereekte H A, et al. Diagnostic usefulness of linear and nonlinear quantitative EEG analysis in Alzheimer's disease. Clin Electroencephalogr, 1996, 27(2): 69-77.
21. Takens F. Detecting strange attractors in turbulence. Lecture Notes in Mathematics, 1981, 898(1): 366-381.
22. 李红利.癫痫脑电信号的非线性分析. 天津: 天津大学, 2012.
23. CAO Liangyue. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 1997, 110(1/2): 43-50.
24. Grassberger J, Procacria I. Measuring the strangeness of strange attractor. Physica D, 1983, 9: 189-208.
25. Janjarasjitt S, Scher M S, Loparo K A. Nonlinear dynamical analysis of the neonatal EEG time series: the relationship between sleep state and complexity. Clin Neurophysiol, 2008, 119(8): 1812-1823.
26. 丁黎明.睡眠脑电的混沌学研究. 镇江: 江苏大学, 2007.
27. Coito A S, Sanches J. Assessment of obstructive sleep apnea syndrome by spectral analysis of physiological parameters//1st Portuguese Meeting in Bioengineering, 2011: 2.
28. Coito A S, Belo D, Paiya T, et al. Topographic EEG brain mapping before, during and after Obstructive Sleep Apnea Episodes// 2011 IEEE International Symposium on/ Biomedical Imaging: From Nano to Macro, 2011: 1860-1863.
29. Akṣahin M, Aydın S, Fırat H, et al. Artificial apnea classification with quantitative sleep EEG synchronization. J Med Syst, 2012, 36(1): 139-144.
30. Almuhammadi W S, Aboalayon K A, Faezipour M. Efficient obstructive sleep apnea classification based on EEG signals//2015 IEEE LONG ISLAND SYSTEMS, APPLICATIONS AND TECHNOLOGY CONFERENCE (LISAT), 2015: 1-6.
31. 江朝晖, 冯焕清, 刘大路, 等. 改进 G-P 算法与睡眠脑电的关联维. 北京生物医学工程, 2004, 23(4): 260-262.

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