Detrended Fluctuation Analysis of Electroencephalogram of Patients with Sleep Apnea Syndrome_Journal of Biomedical Engineering

Authors：

 ZHOUJing , WUXiaoming

School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China;

Corresponding?author：

ZHOUJing, Email: hellozj@scut.edu.cn

Keywords：

sleep apnea syndrome; electroencephalogram; detrended fluctuation analysis; scaling exponents

DOI：

10.7507/1001-5515.20160136

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Sleep apnea syndrome (SAS) is a kind of harmful systemic sleep disorder with high incidence, and the pathological mechanism of it is complicated and the diagnosis and treatment are difficult. Mining the characteristic information of SAS from the single or small physiological signal is a hot topic in the research of sleep disorders in recent years. In our study shown in this paper, the detrended fluctuation analysis (DFA) was used to analyze sleep electroencephalogram (EEG) of SAS patients and normal healthy persons based on the non-stationary and nonlinear characteristics. It was found that in both groups, the scaling exponents increased gradually with the deepening of sleep, and in the rapid eye movement (REM) stage, the scaling exponents decreased. The scaling exponents of SAS group were significantly higher than those of the healthy group. The performance of SAS diagnosis based on scaling exponents was evaluated with receiver operator characteristic (ROC) curve. The optimal threshold value 0.81 for the SAS and normal control were obtained, corresponding to the sensitivity 94.4%, specificity 99.2%, and area under curve (AUC) was 0.994. The results show that DFA scaling exponents have a good discrimination power and accuracy for the SAS, which provide a new theoretical basis for SAS diagnosis.

Citation： ZHOU Jing, WU Xiaoming. Detrended Fluctuation Analysis of Electroencephalogram of Patients with Sleep Apnea Syndrome. Journal of Biomedical Engineering, 2016, 33(5): 842-846. doi: 10.7507/1001-5515.20160136 Copy

1.	GUILLEMINAULT C, TILKIAN A, DEMENT W C. The sleep apnea syndrome[J]. Annu Rev Med, 1976, 27:465-484.
2.	IVANOV P C, BUNDE A, AMARAL L N, et al. Sleep-wake differences in scaling behavior of the human heartbeat:Analysis of terrestrial and long-term space flight data[J]. Europhys Lett, 1999, 48(5):594-600.
3.	ASHKENAZY Y, LEWKOWICZ M, LEVITAN J, et al. Scale-specific and scale-independent measures of heart rate variability as risk indicators[J]. Europhys Lett, 2001, 53(6):709-715.
4.	BUNDE A, HAVLIN S, KANTELHARDT J W, et al. Correlated and uncorrelated regions in heart-rate fluctuations during sleep[J]. Phys Rev Lett, 2000, 85(17):3736-3739.
5.	YUAN Naiming, FU Zuntao, MAO Jiangyu. Different scaling behaviors in daily temperature records over China[J]. Physica A, 2010, 389(19):4087-4095.
6.	IVANOVA K, AUSLOOS M. Application of the detrended fluctuation analysis (DFA) method for describing cloud breaking[J]. Physica A, 1999, 274(1/2):349-354.
7.	TALKNER P, WEBER R O. Power spectrum and detrended fluctuation analysis:Application to daily temperatures[J]. Phys Rev E, 2000, 62(1):150-160.
8.	TELESCA L, BALASCO M, LAPENNA V. Investigating the time correlation properties in self-potential signals recorded in a seismic area of Irpinia, southern Italy[J]. Chaos Solitons Fractals, 2005, 32(1):199-211.
9.	SKORDAS E S, SARLIS N V, VAROTSOS P A. Effect of significant data loss on identifying electric signals that precede rupture estimated by detrended fluctuation analysis in natural time[J]. Chaos, 2010, 20(3):033111.
10.	RUAN Yongping, ZHOU Weixing. Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant[J]. Physica A, 2011, 390(9):1646-1654.
11.	YUAN Y, ZHUANG X T, JIN X. Measuring multifractaliry of stock price fluctuation using multifractal detrended fluctuation analysis[J]. Physica A, 2009, 388:2189-2197.
12.	PENG C K, BULDYREV S V, HAVLIN S, et al. Mosaic organization of DNA nucleotides[J]. Phys Rev, 1994, 49(2):1685-1689.
13.	ABSIL P A, SEPULCHRE R, BILGE A, et al. Nonlinear analysis of cardiac rhythm fluctuations using DFA method[J]. Physica A, 1999, 272(1/2):235-244.
14.	GIFANI P, RABIEE H R, HASHEMI M H, et al. Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification[J]. J Franklin Inst, 2007, 344(3/4):212-229.
15.	LEE J S, YANG B H, LEE J H, et al. Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls[J]. Clin Neurophysiol, 2007, 118(11):2489-2496.
16.	NIKULIN V V, BRISMAR T. Long-range temporal correlations in electroencephalographic oscillations:relation to topography, frequency band, age and gender[J]. Neuroscience, 2005, 130(2):549-558.
17.	宋閏柳, 于靜潔, 劉昌明.基于去趨勢波動分析方法的土壤水分長程相關性研究[J].水利學報, 2011, 42(3):315-322.
18.	寧艷, 江朝暉, 安濱, 等.睡眠生理參數的去趨勢波動分析[J].生物醫學工程學雜志, 2007, 24(2):249-252.
19.	YOUNG T, PEPPARD P, PALTA M, et al. Population-based study of sleep-disordered breathing as a risk factor for hypertension[J]. Arch Intern Med, 1997, 157(15):1746-1752.
20.	DIMSDALE J E, LOREDO J S, PROFANT J. Effect of continuous positive airway pressure on blood pressure:a placebo trial[J]. Hypertension, 2000, 35(1 Pt 1):144-147.
21.	XAVIER P, BEHBEHANI K, WATENPAUGH D, et al, Detecting electroencephalography variations due to sleep disordered breathing events[C]//Proceedings of the 29th IEEE International Conference on Engineering in Medicine and Biology Society. Lyon, 2007:6097-6100.
22.	COITO A L S, SANCHES J. Assessment of obstructive sleep apnea syndrome by spectral analysis of physiological parameters[C]//1st Portuguese Meeting in Biomedical Engineering, 2011:1-6.
23.	TAGLUK M E, SEZGIN N. A new approach for estimation of obstructive sleep apnea syndrome[J]. Expert Syst Appl, 2011, 38(5):5346-5351.

1. GUILLEMINAULT C, TILKIAN A, DEMENT W C. The sleep apnea syndrome[J]. Annu Rev Med, 1976, 27:465-484.
2. IVANOV P C, BUNDE A, AMARAL L N, et al. Sleep-wake differences in scaling behavior of the human heartbeat:Analysis of terrestrial and long-term space flight data[J]. Europhys Lett, 1999, 48(5):594-600.
3. ASHKENAZY Y, LEWKOWICZ M, LEVITAN J, et al. Scale-specific and scale-independent measures of heart rate variability as risk indicators[J]. Europhys Lett, 2001, 53(6):709-715.
4. BUNDE A, HAVLIN S, KANTELHARDT J W, et al. Correlated and uncorrelated regions in heart-rate fluctuations during sleep[J]. Phys Rev Lett, 2000, 85(17):3736-3739.
5. YUAN Naiming, FU Zuntao, MAO Jiangyu. Different scaling behaviors in daily temperature records over China[J]. Physica A, 2010, 389(19):4087-4095.
6. IVANOVA K, AUSLOOS M. Application of the detrended fluctuation analysis (DFA) method for describing cloud breaking[J]. Physica A, 1999, 274(1/2):349-354.
7. TALKNER P, WEBER R O. Power spectrum and detrended fluctuation analysis:Application to daily temperatures[J]. Phys Rev E, 2000, 62(1):150-160.
8. TELESCA L, BALASCO M, LAPENNA V. Investigating the time correlation properties in self-potential signals recorded in a seismic area of Irpinia, southern Italy[J]. Chaos Solitons Fractals, 2005, 32(1):199-211.
9. SKORDAS E S, SARLIS N V, VAROTSOS P A. Effect of significant data loss on identifying electric signals that precede rupture estimated by detrended fluctuation analysis in natural time[J]. Chaos, 2010, 20(3):033111.
10. RUAN Yongping, ZHOU Weixing. Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant[J]. Physica A, 2011, 390(9):1646-1654.
11. YUAN Y, ZHUANG X T, JIN X. Measuring multifractaliry of stock price fluctuation using multifractal detrended fluctuation analysis[J]. Physica A, 2009, 388:2189-2197.
12. PENG C K, BULDYREV S V, HAVLIN S, et al. Mosaic organization of DNA nucleotides[J]. Phys Rev, 1994, 49(2):1685-1689.
13. ABSIL P A, SEPULCHRE R, BILGE A, et al. Nonlinear analysis of cardiac rhythm fluctuations using DFA method[J]. Physica A, 1999, 272(1/2):235-244.
14. GIFANI P, RABIEE H R, HASHEMI M H, et al. Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification[J]. J Franklin Inst, 2007, 344(3/4):212-229.
15. LEE J S, YANG B H, LEE J H, et al. Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls[J]. Clin Neurophysiol, 2007, 118(11):2489-2496.
16. NIKULIN V V, BRISMAR T. Long-range temporal correlations in electroencephalographic oscillations:relation to topography, frequency band, age and gender[J]. Neuroscience, 2005, 130(2):549-558.
17. 宋閏柳, 于靜潔, 劉昌明.基于去趨勢波動分析方法的土壤水分長程相關性研究[J].水利學報, 2011, 42(3):315-322.
18. 寧艷, 江朝暉, 安濱, 等.睡眠生理參數的去趨勢波動分析[J].生物醫學工程學雜志, 2007, 24(2):249-252.
19. YOUNG T, PEPPARD P, PALTA M, et al. Population-based study of sleep-disordered breathing as a risk factor for hypertension[J]. Arch Intern Med, 1997, 157(15):1746-1752.
20. DIMSDALE J E, LOREDO J S, PROFANT J. Effect of continuous positive airway pressure on blood pressure:a placebo trial[J]. Hypertension, 2000, 35(1 Pt 1):144-147.
21. XAVIER P, BEHBEHANI K, WATENPAUGH D, et al, Detecting electroencephalography variations due to sleep disordered breathing events[C]//Proceedings of the 29th IEEE International Conference on Engineering in Medicine and Biology Society. Lyon, 2007:6097-6100.
22. COITO A L S, SANCHES J. Assessment of obstructive sleep apnea syndrome by spectral analysis of physiological parameters[C]//1st Portuguese Meeting in Biomedical Engineering, 2011:1-6.
23. TAGLUK M E, SEZGIN N. A new approach for estimation of obstructive sleep apnea syndrome[J]. Expert Syst Appl, 2011, 38(5):5346-5351.

Journal of Biomedical Engineering

Detrended Fluctuation Analysis of Electroencephalogram of Patients with Sleep Apnea Syndrome

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content