Medical & Biological Engineering & Computing

, Volume 54, Issue 1, pp 133–148 | Cite as

Analysis of A-phase transitions during the cyclic alternating pattern under normal sleep

  • Martin Oswaldo Mendez
  • Ioanna Chouvarda
  • Alfonso Alba
  • Anna Maria Bianchi
  • Andrea Grassi
  • Edgar Arce-Santana
  • Guilia Milioli
  • Mario Giovanni Terzano
  • Liborio Parrino
Original Article

Abstract

An analysis of the EEG signal during the B-phase and A-phases transitions of the cyclic alternating pattern (CAP) during sleep is presented. CAP is a sleep phenomenon composed by consecutive sequences of A-phases (each A-phase could belong to a possible group A1, A2 or A3) observed during the non-REM sleep. Each A-phase is separated by a B-phase which has the basal frequency of the EEG during a specific sleep stage. The patterns formed by these sequences reflect the sleep instability and consequently help to understand the sleep process. Ten recordings from healthy good sleepers were included in this study. The current study investigates complexity, statistical and frequency signal properties of electroencephalography (EEG) recordings at the transitions: B-phase—A-phase. In addition, classification between the onset–offset of the A-phases and B-phase was carried out with a kNN classifier. The results showed that EEG signal presents significant differences (p < 0.05) between A-phases and B-phase for the standard deviation, energy, sample entropy, Tsallis entropy and frequency band indices. The A-phase onset showed values of energy three times higher than B-phase at all the sleep stages. The statistical analysis of variance shows that more than 80 % of the A-phase onset and offset is significantly different from the B-phase. The classification performance between onset or offset of A-phases and background showed classification values over 80 % for specificity and accuracy and 70 % for sensitivity. Only during the A3-phase, the classification was lower. The results suggest that neural assembles that generate the basal EEG oscillations during sleep present an over-imposed coordination for a few seconds due to the A-phases. The main characteristics for automatic separation between the onset–offset A-phase and the B-phase are the energy at the different frequency bands.

Keywords

Sleep CAP Nonlinear analysis Border identification EEG 

References

  1. 1.
    Terzano MG, Parrino L, Smerieri A, Chervin R, Chokroverty S, Guilleminault C, Hirshkowitz M, Mahowald M, Moldofsky H, Rosa A, Thomas R, Walters A (2001) Consensus report. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Med 2:537–553CrossRefPubMedGoogle Scholar
  2. 2.
    Chouvarda I, Rosso V, Mendez MO, Bianchi AM, Parrino L, Grassi A, Terzano MG, Cerutti S (2011) Assessment of the EEG complexity during activations from sleep. Comput Methods Programs Biomed 104:e16–e28CrossRefPubMedGoogle Scholar
  3. 3.
    Terzano MG, Parrino L (1993) Clinical applications of cyclic alternating pattern. Physiol Behav 54:807–813CrossRefPubMedGoogle Scholar
  4. 4.
    Terzano MG, Parrino L, Boselli M, Spaggiari MC, Di Giovanni G (1996) Polysomnographic analysis of arousal responses in OSAS by means of the cyclic alternating pattern (CAP). Clin Neurophysiol 13:145–155CrossRefGoogle Scholar
  5. 5.
    Terzano MG, Parrino L, Spaggiari MC, Palomba V, Rossi M, Smerieri A (2003) CAP variables and arousals as sleep electroencephalogram markers for primary insomnia. Clin Neurophysiol 114:1715–1723CrossRefPubMedGoogle Scholar
  6. 6.
    Terzano MG, Parrino L, Smerieri A, De Carli F, Nobili L, Donadio S, Ferrillo F (2005) CAP and arousals are involved in the homeostatic and ultradian sleep processes. J Sleep Res 14:359–368CrossRefPubMedGoogle Scholar
  7. 7.
    Halasz P, Terzano MG, Parrino L, Bodisz R (2004) The nature of Arousal from sleep. J Sleep Res 13:1–23CrossRefPubMedGoogle Scholar
  8. 8.
    Barcaro U, Bonanni E, Maestri M, Murri L, Parrino L, Terzano MG (2004) A general automatic method for the analysis of NREM sleep microstructure. Sleep Med 5:567–576CrossRefPubMedGoogle Scholar
  9. 9.
    Ferri R, Bruni O, Miano S, Plazzi G, Terzano MG (2005) All-night EEG power spectral analysis of the cyclic alternating pattern components in young adult subjects. Clin Neurophysiol 116:2429–2440CrossRefPubMedGoogle Scholar
  10. 10.
    Ferri R, Bruni O, Miano S, Smerieri A, Spruyt K, Terzano MG (2005) Inter-rater reliability of sleep cyclic alternating pattern (CAP) scoring and validation of a new computer-assisted CAP scoring method. Clin Neurophysiol 116:696–707CrossRefPubMedGoogle Scholar
  11. 11.
    Navona C, Barcaro U, Bonanni E, Di Martino F, Maestri M, Murri L (2002) An automatic method for recognition and classification of the A-phases of the cyclic alternating pattern. Clin Neurophysiol 113:1826–1831CrossRefPubMedGoogle Scholar
  12. 12.
    Mariani S, Manfredini E, Rosso V, Mendez MO, Bianchi AM, Matteucci M, Terzano MG, Cerutti S, Parrino L (2011) Characterization of A phases during the cyclic alternating pattern of sleep. Clin Neurophysiol 22:2016–2024CrossRefGoogle Scholar
  13. 13.
    Mariani S, Manfredini E, Rosso V, Grassi A, Mendez MO, Alba A, Matteucci M, Parrino L, Terzano MG, Cerutti S, Bianchi AM (2012) Efficient automatic classifiers for the detection of A phases of the Cyclic Alternating Pattern in sleep. Med Biol Eng Comput 50:359–372CrossRefPubMedGoogle Scholar
  14. 14.
    Fingelkurts A, Fingelkurts AA (2008) A brain-mind operational architectonics imaging: technical and methodological aspect. Open Neuroimag J 2:73–93CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    American Academy of Sleep Medicine (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, 1st edn. American Academy of Sleep Medicine, Westchester, ILGoogle Scholar
  16. 16.
    Parrino L, Boselli M, Spaggiari MC, Smerieri A, Terzano MG (1998) Cyclic alternating pattern (CAP) in normal sleep: polysomnographic parameters in different age groups, Electroencephalography. Clin Neurophysiol 107:439–450CrossRefGoogle Scholar
  17. 17.
    Accardo A, Affinito M, Carrozzi M, Bouquet F (1997) Use of the fractal dimension for the analysis of electroencephalographic time series. Biol Cybern 77:339–350CrossRefPubMedGoogle Scholar
  18. 18.
    Richman JS, Moorman JM (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ 278:H2039–H2049Google Scholar
  19. 19.
    Kaspar F, Schuster HG (1987) Easily calculable measure for the complexity of spatiotemporal patterns. Phys Rev A 36:842–848CrossRefPubMedGoogle Scholar
  20. 20.
    Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Physica D 31:277–283CrossRefGoogle Scholar
  21. 21.
    Klonowski W (2007) From conformons to human brains: an informal overview of nonlinear dynamics and its applications in biomedicine. Nonlinear Biomedical Physics 1 (5). BioMed Central, London. http://www.nonlinearbiomedphys.com/content/1/1/5
  22. 22.
    Zhang D, Jia X, Ding X, Ye D, Thakor N (2010) Application of tsallis entropy to EEG: quantifying the presence of burst suppression after asphyxial cardiac arrest in rats. IEEE Trans Biomed Eng 57:867–874CrossRefPubMedCentralGoogle Scholar
  23. 23.
    De Carli F, Nobili L, Beelke M, Watanabe T, Smerieri A, Parrino L, Terzano MG, Ferrillo F (2004) Quantitative analysis of sleep EEG microstructure in the time-frequency domain. Brain Res Bull 63:399–405CrossRefPubMedGoogle Scholar
  24. 24.
    Halasz P (1993) Arousals without awakening—dynamic aspect of sleep. Physiol Behav 54:795–802CrossRefPubMedGoogle Scholar
  25. 25.
    Kaplan A, Shishkin SL (2000) Application of the change-point analysis to the investigation of the brain’s electrical activity (Chapter 7). In: Brodsky BE, Darkhovsky BS (eds) Nonparametric statistical diagnosis: problems and methods. Kluwer Academic Publishers, Dordrecht, pp 333–338CrossRefGoogle Scholar
  26. 26.
    Ferri R, Rundo F, Bruni O, Terzano MG, Stam CJ (2008) The functional connectivity of different EEG bands moves toward small-world network organization during sleep. Clin Neurophysiol 119:2026–2036CrossRefPubMedGoogle Scholar
  27. 27.
    Ferri R, Rundo F, Bruni O, Terzano MG, Stam CJ (2006) Regional scalp EEG slow-wave synchronization during sleep cyclic alternating pattern A1 subtypes. Neurosci Lett 404:352–357CrossRefPubMedGoogle Scholar
  28. 28.
    Ferri R, Rundo F, Bruni O, Terzano MG, Stam CJ (2005) Dynamics of the EEG slow-wave synchronization during sleep. Clin Neurophysiol 116:2783–2795CrossRefPubMedGoogle Scholar
  29. 29.
    Sciarretta G, Bricolo A (1970) Automatic detection of sleep spindles by analysis of harmonic components. Med Biol Eng Comput 8:517–519CrossRefGoogle Scholar
  30. 30.
    Hao YL, Ueda Y, Ishii N (1992) Improved procedure of complex demodulation and an application to frequency analysis of sleep spindles in EEG. Med Biol Eng Comput 30:406–412CrossRefPubMedGoogle Scholar
  31. 31.
    Huupponen E, Himanen SL, Hasa J, Varri A (2003) Automatic analysis of electro-encephalogram sleep spindle frequency throughout the night. Med Biol Eng Comput 41:727–732CrossRefPubMedGoogle Scholar
  32. 32.
    Mendez MO, Bianchi AM, Montano N, Patruno V, Gil E, Mantaras C, Aiolfi A, Cerutti S (2008) On arousal from sleep: time-frequency analysis. Med Biol Eng Comput 46:341–351CrossRefPubMedGoogle Scholar
  33. 33.
    Ferri R, Parrino L, Smerieri A, Terzano MG, Elia M, Stam CJ (2002) Non-linear EEG measures during sleep: effects of the different sleep stages and cyclic alternating pattern. Int J Phychophysiol 43:273–286CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2015

Authors and Affiliations

  • Martin Oswaldo Mendez
    • 1
  • Ioanna Chouvarda
    • 2
  • Alfonso Alba
    • 1
  • Anna Maria Bianchi
    • 3
  • Andrea Grassi
    • 4
  • Edgar Arce-Santana
    • 1
  • Guilia Milioli
    • 4
  • Mario Giovanni Terzano
    • 4
  • Liborio Parrino
    • 4
  1. 1.Facultad de CienciasUniversidad Autónoma de San Luis PotosíSan Luis Potosí (SLP)Mexico
  2. 2.Lab of Medical InformaticsAristotle University of ThessalonikiThessalonikiGreece
  3. 3.Biomedical Engineering DepartmentPolitecnico di MilanoMilanItaly
  4. 4.Department of Neurology, Sleep Disorders CentreUniversity of ParmaParmaItaly

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