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Adaptation of recurrence plot method to study a polysomnography: changes in EEG activity in obstructive sleep apnea syndrome

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Abstract

The aim of this work is to numerically evaluate the characteristics of the stages of nocturnal sleep, recorded in patients with respiratory disorders compared with a group of healthy participants in clinical trials. Based on the use of recurrent analysis, it was shown that the recurrence parameters of the two nocturnal sleep recordings, both for patients with severe obstructive sleep apnea syndrome and for healthy participants, do not show statistical differences. At the same time, comparison of the recurrent characteristics of the stages of nocturnal sleep in the two groups demonstrate statistical differences that are promising for further analysis. During the deepest stages of NREM sleep (N3 and N4), the R characteristics of healthy participants are reduced and significantly homogeneous against the background of heterogeneous results of the group of OSA patients. The opposite situation is observed for stage N2, at which the dynamics of RA characteristics in OSA patients becomes monotonous, repeating from participant to participant. The maximum differences for apparently healthy participants and OSA patients were observed during the REM sleep stage, when the \(<RR_{stage}>\) value increased significantly in the presence of respiratory disorders.

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Data availability statement

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. A. Runnova, M. Zhuravlev, R. Ukolov, I. Blokhina, A. Dubrovski, N. Lezhnev, E. Sitnikova, E. Saranceva, A. Kiselev, A. Karavaev et al., Modified wavelet analysis of ECOG-pattern as promising tool for detection of the blood-brain barrier leakage. Sci. Rep. 11(1), 1–8 (2021)

    Google Scholar 

  2. K. Sergeev, A. Runnova, M. Zhuravlev, O. Kolokolov, N. Akimova, A. Kiselev, A. Titova, A. Slepnev, N. Semenova, T. Penzel, Wavelet skeletons in sleep EEG-monitoring as biomarkers of early diagnostics of mild cognitive impairment. Chaos: Interdisciplinary. J Nonlinear. Sci. 31(7), 073110 (2021)

    Google Scholar 

  3. C.S. Royce, M.M. Hayes, R.M. Schwartzstein, Teaching critical thinking: a case for instruction in cognitive biases to reduce diagnostic errors and improve patient safety. Acad. Med. 94(2), 187–194 (2019)

    Google Scholar 

  4. A. Haleem, R. Vaishya, M. Javaid, I.H. Khan, Artificial intelligence (AI) applications in orthopaedics: an innovative technology to embrace. J. Clin. Orthopaedics. Trauma. 11(Suppl 1), 80 (2020)

    Google Scholar 

  5. M. Zhuravlev, A. Runnova, K. Smirnov, E. Sitnikova, Spike-wave seizures, NREM sleep and micro-arousals in WAG/Rij rats with genetic predisposition to absence epilepsy: developmental aspects. Life 12(4), 576 (2022)

    ADS  Google Scholar 

  6. E. Sitnikova, A.E. Hramov, V. Grubov, A.A. Koronovsky, Time-frequency characteristics and dynamics of sleep spindles in WAG/Rij rats with absence epilepsy. Brain. Res. 1543, 290–299 (2014)

    Google Scholar 

  7. Y. Fujisawa, S. Inoue, Y. Nakamura, The possibility of deep learning-based, computer-aided skin tumor classifiers. Front. Med. 6, 191 (2019)

    Google Scholar 

  8. N.M. Ralbovsky, I.K. Lednev, Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chem. Soc. Rev. 49(20), 7428–7453 (2020)

    Google Scholar 

  9. N. Zhang, Y.-X. Cai, Y.-Y. Wang, Y.-T. Tian, X.-L. Wang, B. Badami, Skin cancer diagnosis based on optimized convolutional neural network. Artif. Intell. Med. 102, 101756 (2020)

    Google Scholar 

  10. A. Klingenstein, A. Garip-Kuebler, U.G. Mueller-Lisse, C. Hintschich, Combined positron emission tomography/computed tomography for diagnosis and monitoring of orbital adnexal lymphoma. Acta. Ophthalmologica. 96(6), 712–717 (2018)

    Google Scholar 

  11. H. Singh, R.S. Solanki, Classification & feature extraction of brain tumor from MRI images using modified ANN approach. Int. J. Electrical. Electronics. Res. (IJEER). 9, 10–15 (2021)

    Google Scholar 

  12. A.H. Ribeiro, M.H. Ribeiro, G.M. Paixão, D.M. Oliveira, P.R. Gomes, J.A. Canazart, M.P. Ferreira, C.R. Andersson, P.W. Macfarlane, W. Meira Jr. et al., Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat. Commun. 11(1), 1–9 (2020)

    Google Scholar 

  13. O. Faust, U.R. Acharya, Automated classification of five arrhythmias and normal sinus rhythm based RR interval signals. Expert. Syst. Appl. 181, 115031 (2021)

    Google Scholar 

  14. A. Adami, C. Gentile, T. Hepp, G. Molon, G.L. Gigli, M. Valente, V. Thijs, Electrocardiographic RR interval dynamic analysis to identify acute stroke patients at high risk for atrial fibrillation episodes during stroke unit admission. Transl. Stroke. Res. 10(3), 273–278 (2019)

    Google Scholar 

  15. A.-A. Baril, J. Carrier, A. Lafrenière, S. Warby, J. Poirier, R.S. Osorio, N. Ayas, M.-P. Dubé, D. Petit, N. Gosselin et al., Biomarkers of dementia in obstructive sleep apnea. Sleep Med. Rev. 42, 139–148 (2018)

    Google Scholar 

  16. A.E. Mullins, K. Kam, A. Parekh, O.M. Bubu, R.S. Osorio, A.W. Varga, Obstructive sleep apnea and its treatment in aging: effects on Alzheimer’s disease biomarkers, cognition, brain structure and neurophysiology. Neurobiol. Dis. 145, 105054 (2020)

    Google Scholar 

  17. M. Díaz-Román, M.M. Pulopulos, M. Baquero, A. Salvador, A. Cuevas, I. Ferrer, O. Ciopat, E. Gómez, Obstructive sleep apnea and Alzheimer’s disease-related cerebrospinal fluid biomarkers in mild cognitive impairment. Sleep 44(1), 133 (2021)

    Google Scholar 

  18. T.Y. Siow, C.H. Toh, J.L. Hsu, G.H. Liu, S.H. Lee, N.H. Chen, C.J. Fu, M. Castillo, J.T. Fang, Association of sleep, neuropsychological performance, and gray matter volume with glymphatic function in community-dwelling older adults. Neurology 98(8), 829–838 (2022)

    Google Scholar 

  19. I. Imayama, B. Prasad, Role of leptin in obstructive sleep apnea. Ann. Am. Thoracic. Soc. 14(11), 1607–1621 (2017)

    Google Scholar 

  20. J.-P. Eckmann, S. Kamphorst, D. Ruelle, Recurrence plots of dynamical systems. Europhys. Lett. 4(9), 973–977 (1987)

    ADS  Google Scholar 

  21. U.R. Acharya, S.V. Sree, S. Chattopadhyay, W. Yu, P.C.A. Ang, Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. Int. J. Neural. Syst. 21(03), 199–211 (2011)

    Google Scholar 

  22. K. Becker, G. Schneider, M. Eder, A. Ranft, E.F. Kochs, W. Zieglgänsberger, H.-U. Dodt, Anaesthesia monitoring by recurrence quantification analysis of EEG data. PloS One 5(1), 8876 (2010)

    ADS  Google Scholar 

  23. Z. Xiong, M.P. Nash, E. Cheng, V.V. Fedorov, M.K. Stiles, J. Zhao, ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiological. Measurement. 39(9), 094006 (2018)

    ADS  Google Scholar 

  24. A. Runnova, A. Selskii, E. Emelyanova, M. Zhuravlev, M. Popova, A. Kiselev, R. Shamionov, Modification of joint recurrence quantification analysis (JRQA) for assessing individual characteristics from short EEG time series. Chaos: Interdisciplinary. J. Nonlinear. Sci. 31(9), 093116 (2021)

    Google Scholar 

  25. J. Fell, J. Röschke, K. Mann, C. Schäffner, Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. Electroencephalography. Clin. Neurophysiol. 98(5), 401–410 (1996)

    Google Scholar 

  26. A.D. Krystal, C. Zaidman, H.S. Greenside, R.D. Weiner, C.E. Coffey, The largest lyapunov exponent of the EEG during ECT seizures as a measure of ECT seizure adequacy. Electroencephalography. Clin. Neurophysiol. 103(6), 599–606 (1997)

    Google Scholar 

  27. E. Pereda, A. Gamundi, M. Nicolau, R. Rial, J. González, Interhemispheric differences in awake and sleep human EEG: a comparison between non-linear and spectral measures. Neurosci. Lett. 263(1), 37–40 (1999)

    Google Scholar 

  28. E. Pereda, A. Gamundi, R. Rial, J. González, Non-linear behaviour of human EEG: fractal exponent versus correlation dimension in awake and sleep stages. Neurosci. Lett. 250(2), 91–94 (1998)

    Google Scholar 

  29. R. Ferri, L. Parrino, A. Smerieri, M.G. Terzano, M. Elia, S.A. Musumeci, S. Pettinato, C.J. Stam, Non-linear EEG measures during sleep: effects of the different sleep stages and cyclic alternating pattern. Int. J. Psychophysiol. 43(3), 273–286 (2002)

    Google Scholar 

  30. Y. Shen, E. Olbrich, P. Achermann, P. Meier, Dimensional complexity and spectral properties of the human sleep EEG. Clin. Neurophysiol. 114(2), 199–209 (2003)

    Google Scholar 

  31. J. Theiler, Spurious dimension from correlation algorithms applied to limited time-series data. Phys. Rev. A. 34(3), 2427 (1986)

    ADS  Google Scholar 

  32. N. Thomasson, T.J. Hoeppner, C.L. Webber Jr., J.P. Zbilut, Recurrence quantification in epileptic EEGs. Phys. Lett. A. 279(1–2), 94–101 (2001)

    ADS  Google Scholar 

  33. T. Schreiber, A. Schmitz, Surrogate time series. Physica D.: Nonlinear. Phenomena. 142(3–4), 346–382 (2000)

    ADS  MathSciNet  MATH  Google Scholar 

  34. C.L. Webber Jr., J.P. Zbilut, Dynamical assessment of physiological systems and states using recurrence plot strategies. J. App. Physiol. 76(2), 965–973 (1994)

    Google Scholar 

  35. E. Olbrich, P. Achermann, P. Meier, Dynamics of human sleep EEG. Neurocomputing 52, 857–862 (2003)

    Google Scholar 

  36. U.R. Acharya, S.V. Sree, G. Swapna, R.J. Martis, J.S. Suri, Automated EEG analysis of epilepsy: a review. Knowl-Based. Sys. 45, 147–165 (2013)

    Google Scholar 

  37. N. Marwan, M.C. Romano, M. Thiel, J. Kurths, Recurrence plots for the analysis of complex systems. Phys. Rep. 438(5–6), 237–329 (2007)

    ADS  MathSciNet  Google Scholar 

  38. N. Marwan, J. Kurths, Nonlinear analysis of bivariate data with cross recurrence plots. Phys. Lett. A. 302, 299–307 (2002)

    ADS  MathSciNet  MATH  Google Scholar 

  39. N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan, J. Kurths, Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys. Rev. E. 66(2), 026702 (2002)

    ADS  MATH  Google Scholar 

  40. B.M. Mathunjwa, Y.T. Lin, C.H. Lin, M.F. Abbod, J.S. Shieh, ECG arrhythmia classification by using a recurrence plot and convolutional neural network. Biomed. Signal Proc. Control 64, 102262 (2021)

    Google Scholar 

  41. E. Garcia-Ceja, M.Z. Uddin, J. Torresen, Classification of recurrence plots’ distance matrices with a convolutional neural network for activity recognition. Procedia. Computer. Sci. 130, 157–163 (2018)

    Google Scholar 

  42. J. Zbilut, A. Giuliani, C.L. Webber Jr., Detecting deterministic signals in exceptionally noisy environments using cross-recurrence quantification. Phys. Lett. A. 246, 122–128 (1998)

    ADS  Google Scholar 

  43. W.R. Ruehland, P.D. Rochford, F.J. O’Donoghue, R.J. Pierce, P. Singh, A.T. Thornton, The new AASM criteria for scoring hypopneas: impact on the apnea hypopnea index. Sleep 32(2), 150–157 (2009)

    Google Scholar 

  44. A. Martin, G. Guerrero-Mora, G. Dorantes-Mèndez, A. Alba, M.O. Mèndez, I. Chouvarda, Non-linear analysis of EEG and HRV signals during sleep. IEEE Eng. Med. Biol. Soc. 2015, 4174–4177 (2015)

    Google Scholar 

  45. C. Frilot, D.E. McCarty, A.A. Marino, An original method for staging sleep based on dynamical analysis of a single EEG signal. J. Neurosci. Methods. 308, 135–141 (2018)

    Google Scholar 

  46. R. Acharya, O. Faust, N. Kannathal, T. Chua, S. Laxminarayan, Non-linear analysis of EEG signals at various sleep stages. Computer. Methods. Programs. Biomed. 80(1), 37–45 (2005)

    Google Scholar 

  47. I.H. Song, D.S. Lee, S.I. Kim, Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans. Neurosci. Lett. 366(2), 148–153 (2004)

    Google Scholar 

  48. A. Runnova, M. Zhuravlev, A. Kiselev, R. Ukolov, K. Smirnov, A. Karavaev, E. Sitnikova, Automatic wavelet-based assessment of behavioral sleep using multichannel electrocorticography in rats. Sleep. Breathing. 25(4), 2251–2258 (2021)

    Google Scholar 

  49. H.K. Meeren, J.P. Pijn, E.L. Van Luijtelaar, A.M. Coenen, F.H. Lopes da Silva, Cortical focus drives widespread corticothalamic networks during spontaneous absence seizures in rats. J. Neurosci. Off. J. Soc. Neurosci. 22(4), 1480–1495 (2002)

    Google Scholar 

  50. S. Xu, O. Faust, S. Seoni, S. Chakraborty, P.D. Barua, H.W. Loh, H. Elphick, F. Molinari, U.R. Acharya, A review of automated sleep disorder detection. Computers. Biol. Med. 150, 106100 (2022)

    Google Scholar 

  51. H. Poincare, Sur le probleme des trois corps et les equations de la dynamique. Acta. Math. 13, 1–270 (1890)

    MATH  Google Scholar 

  52. J. West, Z. Dasht Bozorgi, J. Herron, H.J. Chizeck, J.D. Chambers, L. Li, Machine learning seizure prediction: one problematic but accepted practice. J. Neural. Eng. 20(1) (2023)

  53. R. Esposito, M. Bortoletto, C. Miniussi, Integrating TMS, EEG, and MRI as an approach for studying brain connectivity. Neuroscientist 26(5–6), 471–486 (2020)

    Google Scholar 

  54. M.I. Garrido, J.M. Kilner, S.J. Kiebel, K.J. Friston, Evoked brain responses are generated by feedback loops. Proc. National. Acad. Sci. 104(52), 20961–20966 (2007)

    ADS  Google Scholar 

  55. W. Klimesch, EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain. Res. Rev. 29(2–3), 169–195 (1999)

    Google Scholar 

  56. W. Klimesch, Memory processes, brain oscillations and EEG synchronization. Int. J. Psychophysiol. 24(1–2), 61–100 (1996)

    Google Scholar 

  57. B. Kemp, A.H. Zwinderman, B. Tuk, H.A. Kamphuisen, J.J. Oberye, Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans. Biomed. Eng. 47(9), 1185–1194 (2000)

    Google Scholar 

  58. A.R. Adamantidis, C. Gutierrez Herrera, T.C. Gent, Oscillating circuitries in the sleeping brain. Nat. Rev. Neurosci. 20(12), 746–762 (2019)

    Google Scholar 

  59. J. Choi, M. Kwon, S.C. Jun, A systematic review of closed-loop feedback techniques in sleep studies-related issues and future directions. Sensors 20(10), 2770 (2020)

    ADS  Google Scholar 

  60. I.-H. Song, D.-S. Lee, S.I. Kim, Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans. Neurosci. Lett. 366(2), 148–153 (2004)

    Google Scholar 

  61. L. Marshall, N. Cross, S. Binder, T.T. Dang-Vu, Brain rhythms during sleep and memory consolidation: neurobiological insights. Physiology 35(1), 4–15 (2020)

    Google Scholar 

  62. K.A. Paller, A. Mayes, J. Antony, K.A. Norman, Replay-based consolidation governs enduring memory storage. Cogn. Neurosci. 263–274 (2020)

  63. I.E. Djonlagic, M. Guo, M. Igue, D. Kishore, R. Stickgold, A. Malhotra, Continuous positive airway pressure restores declarative memory deficit in obstructive sleep apnea. Am. J. Respiratory. Critical Care. Med. 203(9), 1188–1190 (2021)

    Google Scholar 

  64. A.D. Likens, K.S. McCarthy, L.K. Allen, D.S. McNamara, Recurrence quantification analysis as a method for studying text comprehension dynamics. Neurosci. Lett. LAK’18, 111–120 (2018)

  65. A. Fabretti, M. Ausloos, Recurrence plot and recurrence quantification analysis techniques for detecting a critical regime. J. Modern. Phys. C. 16(5), 671–706 (2005)

    ADS  MATH  Google Scholar 

  66. N.C. Anderson, W.F. Bischof, K.E.W. Laidlaw, E.F. Risko, A. Kingstone, Recurrence quantification analysis of eye movements. Behav. Res. Methods. 45, 842–856 (2013)

    Google Scholar 

  67. S. Martin-Gonzalez, J.L. Navarro-Mesa, G. Julia-Serda, G.M. Ramirez-Avila, A.G. Ravelo-Garcia, Improving the understanding of sleep apnea characterization using recurrence quantification analysis by defining overall acceptable values for the dimensionality of the system the delay and the distance threshold. PLoS One 13(4), 0194462 (2018)

    Google Scholar 

  68. E.S. Jackson, M. Tiede, M.A. Riley, D.H. Whalen, Recurrence quantification analysis of sentence-level speech kinematics. J. Speech. Lang. Hear. Res. 59(6), 1315–1326 (2018)

    Google Scholar 

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Acknowledgements

The preparation of clinical material, statistical analysis of the results and their interpretation have been partially supported by of the Government Procurement of the Russian Federation Ministry of Healthcare within the state assignment “Development of algorithms for recognizing markers of breathing disorders during sleep in patients with various forms of cardiovascular pathology” No 122013100209-5 (2022–2024), performed in National Medical Research Center for Therapy and Preventive Medicine. Within the framework of the Russian Science Foundation (Project No. 22-72-10061), an adaptation of the assessment of parameters rates and the corresponding computational work were performed.

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Authors and Affiliations

  1. Institute of Physics, Saratov State University, Saratov, 410012, Russia

    Anton Selskii & Maksim Zhuravlev

  2. Center for Coordination of Fundamental Scientific Activities, National Medical Research Center for Therapy and Preventive Medicine, Moscow, 101990, Russia

    Oxana Drapkina, Mikhail Agaltsov & Anastasiya Runnova

  3. Institute of Cardiological Research, Saratov State Medical University, Saratov, 410005, Russia

    Olga Posnenkova, Margarita Simonyan, Maksim Zhuravlev & Anastasiya Runnova

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  1. Anton Selskii
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S.I.: Brain Physiology Meets Complex Systems. Guest editors: Thomas Penzel, Teemu Myllylä, Oxana V. Semyachkina-Glushkovskaya, Alexey Pavlov, Anatoly Karavaev.

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Selskii, A., Drapkina, O., Agaltsov, M. et al. Adaptation of recurrence plot method to study a polysomnography: changes in EEG activity in obstructive sleep apnea syndrome. Eur. Phys. J. Spec. Top. 232, 703–714 (2023). https://doi.org/10.1140/epjs/s11734-023-00814-8

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