Chaos-Based Analysis of Heart Rate Variability Time Series in Obstructive Sleep Apnea Subjects

Shiva Naghsh, Mohammad Ataei, Mohammadreza Yazdchi, Mohammad Hashemi

DOI: 10.4103/jmss.JMSS_23_19

Abstract


Obstructive sleep apnea (OSA) is a common disorder which can cause periodic fluctuations in heart rate. To diagnose sleep apnea, some studies analyze electrocardiogram (ECG) signals by adopting chaos-based analysis. This research is going to specifically focus on whether it is possible to use chaos-based analysis of heart rate variability (HRV) signals rather than using chaotic analysis of ECG signals to diagnose OSA. While conventional studies mostly use chaos-based analysis of ECG signals to detect OSA, here, we apply correlation dimension (CD) as a chaotic index to analyze HRV data in OSA patients. For this purpose, 17 patients with OSA and 9 healthy individuals referred to a sleep clinic in Isfahan/Iran are studied, and their HRV time series were extracted from 1-h ECG signals recorded overnight. The preliminary step to calculate CD is phase-space reconstruction of the system based on HRV time series. Corresponding parameters, including embedding dimension and lag time, are estimated optimally using enhanced related methods, and then CD is calculated using Grassberger-Procaccia algorithm. Moreover, to evaluate our results, detrended fluctuation analysis (DFA), one of the well-known nonlinear methods in HRV analysis to detect OSA, is also applied to our data and the result is compared with those obtained from CD analysis of HRV. CD index with P < 0.005 indicates a significant difference in nonlinear dynamics of HRV signals detected from OSA patients and healthy individuals.

Keywords


Chaotic indexes, correlation dimension, detrended fluctuation analysis, heart rate variability, obstructive sleep apnea

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References


Epstein LJ, Kristo D, Strollo PJ Jr., Friedman N, Malhotra A, Patil SP, et al. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med 2009;5:263-76. Back to cited text no. 1

Findley LJ, Unverzagt ME, Suratt PM. Automobile accidents involving patients with obstructive sleep apnea. Am Rev Respir Dis 1988;138:337-40. Back to cited text no. 2

Terán-Santos J, Jiménez-Gómez A, Cordero-Guevara J. The association between sleep apnea and the risk of traffic accidents. Cooperative group burgos-santander. N Engl J Med 1999;340:847-51. Back to cited text no. 3

Shahar E, Whitney CW, Redline S, Lee ET, Newman AB, Nieto FJ, et al. Sleep-disordered breathing and cardiovascular disease: Cross-sectional results of the sleep heart health study. Am J Respir Crit Care Med 2001;163:19-25. Back to cited text no. 4

Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med 2000;342:1378-84. Back to cited text no. 5

Adlakha A, Shepard JW Jr. Cardiac arrhythmias during normal sleep and in obstructive sleep apnea syndrome. Sleep Med Rev 1998;2:45-60. Back to cited text no. 6

Guilleminault C, Connolly S, Winkle R, Melvin K, Tilkian A. Cyclical variation of the heart rate in sleep apnoea syndrome. Mechanisms, and usefulness of 24 h electrocardiography as a screening technique. Lancet 1984;1:126-31. Back to cited text no. 7

Narkiewicz K, Somers VK. Sympathetic nerve activity in obstructive sleep apnoea. Acta Physiol Scand 2003;177:385-90. Back to cited text no. 8

Narkiewicz K, Pesek CA, Kato M, Phillips BG, Davison DE, Somers VK, et al. Baroreflex control of sympathetic nerve activity and heart rate in obstructive sleep apnea. Hypertension 1998;32:1039-43. Back to cited text no. 9

Sforza E, Grandin S, Jouny C, Rochat T, Ibanez V. Is waking electroencephalographic activity a predictor of daytime sleepiness in sleep-related breathing disorders? Eur Respir J 2002;19:645-52. Back to cited text no. 10

O'Brien C, Heneghan C. A comparison of algorithms for estimation of a respiratory signal from the surface electrocardiogram. Comput Biol Med 2007;37:305-14. Back to cited text no. 11

Mendez MO, Ruini DD, Villantieri OP, Matteucci M, Penzel T, Cerutti S, et al. Detection of Sleep Apnea from Surface ECG Based on Features Extracted by an Autoregressive Model. 29th Annual International Conference of the IEEE; 2007. p. 6105-8. Back to cited text no. 12

Alvarez D, Hornero R, Marcos JV, del Campo F. Multivariate analysis of blood oxygen saturation recordings in obstructive sleep apnea diagnosis. IEEE Trans Biomed Eng 2010;57:2816-24. Back to cited text no. 13

Goldshtein E, Tarasiuk A, Zigel Y. Automatic detection of obstructive sleep apnea using speech signals. IEEE Trans Biomed Eng 2011;58:1373-82. Back to cited text no. 14

Dingli K, Assimakopoulos T, Wraith PK, Fietze I, Witt C, Douglas NJ, et al. Spectral oscillations of RR intervals in sleep apnoea/hypopnoea syndrome patients. Eur Respir J 2003;22:943-50. Back to cited text no. 15

Steven V. Heart Rate Variability: Linear and Non-Linear Analysis with Applications in Humans Physiology. Doctaral Thesis of Faculty of Electrical Engineering Kasteelpark Arenberg. Belgium; 2010. Back to cited text no. 16

Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim CM, Suri JS. Heart rate variability: A review. Med Biol Eng Comput 2006;44:1031-51. Back to cited text no. 17

Goldberger AL, West BJ. Applications of nonlinear dynamics to clinical cardiology. Ann N Y Acad Sci 1987;504:195-213. Back to cited text no. 18

Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Trans A Math Phys Eng Sci 2009;367:277-96. Back to cited text no. 19

Penzel T, Kantelhardt JW, Grote L, Peter JH, Bunde A. Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Trans Biomed Eng 2003;50:1143-51. Back to cited text no. 20

Al-Angari HM, Sahakian AV. Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome. IEEE Trans Biomed Eng 2007;54:1900-4. Back to cited text no. 21

Miyata M, Burioka N, Sako T, Suyama H, Fukuoka Y, Tomita K, et al. A short daytime test using correlation dimension for respiratory movement in OSAHS. Eur Respir J 2004;23:885-90. Back to cited text no. 22

Miyata M, Burioka N, Suyama H, Sako T, Nomura T, Takeshima T, et al. Non-linear behaviour of respiratory movement in obstructive sleep apnoea syndrome. Clin Physiol Funct Imaging 2002;22:320-7. Back to cited text no. 23

Moeynoi P, Kitjaidure Y. Canonical Correlation Analysis for Dimensionality Reduction of Sleep Apnea Features Based on ECG Single Lead. 2016 9th Biomedical Engineering International Conference (BMEiCON). Laung Prabang. Laos; 2016. p. 1-5. Back to cited text no. 24

Acharya UR, Chua EC, Faust O, Lim TC, Lim LF. Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters. Physiol Meas 2011;32:287-303. Back to cited text no. 25

Jafari A. Sleep apnoea detection from ECG using features extracted from reconstructed phase space and frequency domain. Biomed Signal Process Control 2013;8:551-8. Back to cited text no. 26

Zapanta L, Poon CS, White DP, Marcus CL, Katz ES. Heart Rate Chaos in Obstructive Sleep Apnea in Children. In Proceedings of the 26th Annual IEEE Engineering in Medicine and Biology Conference. New York, USA. IEEE EMBS; 2006. p. 3565-8. Back to cited text no. 27

Jazi MH, Amra B, Yazdchi MR, Jahangiri M, Tabesh F, Gholamrezaei A, et al. P wave duration and dispersion in holter electrocardiography of patients with obstructive sleep apnea. Sleep Breath 2014;18:549-54. Back to cited text no. 28

Takens F. Detecting strange attractors in turbulence. Dynamical Systems and Turbulence. Warwick, Germany. Springer; 1980. p. 366-81. Back to cited text no. 29

Fraser AM, Swinney HL. Independent coordinates for strange attractors from mutual information. Phys Rev A Gen Phys 1986;33:1134-40. Back to cited text no. 30

Ataei M, Sedigh AK, Lohmann B, Lucas C. Determining Embedding Dimension from Output Time Series of Dynamical Systems- Scalar and Multiple Output Cases. Proceedings of the International Conference on System Identification and Control Problems. SICPRO03. Moscow; 2003. Back to cited text no. 31

Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. Physica D 1983;9:189-208. Back to cited text no. 32

Franaszek M. Optimized algorithm for the calculation of correlation integrals. Phys Rev A Gen Phys 1989;39:5440-3. Back to cited text no. 33

Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995;5:82-7. Back to cited text no. 34

Boneau CA. The effects of violations of assumptions underlying the test. Psychol Bull 1960;57:49-64. Back to cited text no. 35

Faust O, Acharya UR, Molinari F, Chattopadhyay S, Tamura T. Linear and non-linear analysis of cardiac health in diabetic subjects. Biomed Signal Process Control 2012;7:295-302 Back to cited text no. 36


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