Somnologie - Schlafforschung und Schlafmedizin

, Volume 11, Issue 3, pp 202–210 | Cite as

Segmental structure of alpha waves in sleep-deprived subjects

  • D. A. Putilov
  • E. G. Verevkin
  • O. G. Donskaya
  • A. A. Putilov
ORIGINAL ARTICLE

Summary

Aim of the study

The quasi-stationary segments of an EEG signal can represent temporary stable local microstates in brain activity, whereas the transition points between the adjacent segments can signify relatively rapid shifts from one such microstate to another. The procedure of adaptive segmentation allows identification of the transition points with abrupt amplitude changes and cutting of an EEG record on the quasi-stationary segments. We tested the applicability of this procedure for quantification of the response of alpha waves to sleep deprivation.

Patients and methods

EEGs were recorded in 39 volunteers at 3-h intervals over the course of more than 24 waking hours. The records were bandpass filtered (7.00–12.99 Hz) and subjected to automatic segmentation (SECTION 0.1®,Human Brain Research Group, Moscow State University).

Results

The characteristics of high amplitude quasi-stationary segments – segment's length, averaged across segment amplitude and coefficient of variation of within-segmental amplitude – were elevated in the sleep-deprived subjects in the eyes open condition. Such elevations can reflect the decrease in stability of functional synchronization within neuronal assemblies along with the increase in their size and life span.

Conclusions

A night without sleep significantly differs in the segmental structure of alpha waves from a night with sleep.

Key words

waking EEG alpha attenuation test regulation of the sleep-wake cycle sleep pressure automatic segmentation of alpha rhythm chronotype 

Untersuchung der Segmentstruktur von Alpha-Wellen bei Schlaflosigkeit

Zusammenfassung

Das Ziel der Forschung

Die quasi-stationären Segmente des EEG-Signals können als zeitlich stabile lokale Mikro-Zustände der Gehirntätigkeit dargestellt werden,wobei die Übergänge zwischen aufeinander folgenden Segmenten die relativ schnelle Änderung eines solchen Mikrozustandes in den anderen bedeuten können. Die adaptive Segmentierung erlaubt es, die Übergangspunkte mit drastischen Änderungen der Amplitude zu identifizieren und die EEG-Aufzeichnung in quasi-stationäre Segmente zu schneiden.Wir haben die Anwendung dieser Prozedur für die quantitative Beschreibung der Alpha-Wellen auf die Schlaflosigkeit untersucht.

Patienten und Methoden

Das EEG wurde bei 39 Volontären mit einem 3- Stunden-Takt in mehr als 24 Stunden des Wachzustandes aufgenommen. Die Registrierung wurde im Frequenzbereich von 7.00-12.99 Hz gefiltert und automatisch segmentiert (SEKTION 0.1,Human Brain Research Group,Moscow State University).

Ergebnisse

Die Merkmale von quasi-stationären Hochamplituden- Segmenten – die Segmentlänge, die mittlere Amplitude des Segments und der Koeffizient der Amplitudenvariation im Segment – waren nach der Schlaflosigkeit vergrößert. Eine solche Veränderung zeigt die verringerte Stabilität der funktionalen Synchronisation im Neutronen Ensemble, sowie auch ihre Vergrößerung und die Verlängerung der Existenzdauer.

Fazit

Die schlaflose Nacht unterscheidet sich von einer Nacht mit gutem Schlaf in der Struktur von Alpha-Wellen signifikant.

Schlüsselwörter

Das EEG des Wachzustandes Test der Alpha- Erniedrigung Regulierung des Schlaf-Wachzustand-Zyklus Schlafdruck automatische Segmentierung des Alpha-Rhythmus Chronotyp 

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References

  1. 1.
    Åkerstedt T, Gillberg M (1990) Subjective and objective sleepiness in the active individual. Int J Neurosci 52:29–37PubMedCrossRefGoogle Scholar
  2. 2.
    Basar E, Schurmann M,Basar-Eroglu C, Karakas S (1997) Alpha oscillations in brain functioning: an integrative theory. Int J Psychophysiol 26:5–29PubMedCrossRefGoogle Scholar
  3. 3.
    Bodenstein G, Praetorius HM (1977) Feature extraction from the electroencephalogram by adaptive segmentation. Proc IEEE 65:642–652CrossRefGoogle Scholar
  4. 4.
    Cajochen C, Khalsa SBS, Wyatt JK, Czeisler CA, Dijk DJ (1999) EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss. Am J Physiol 277:R640–R649PubMedGoogle Scholar
  5. 5.
    Curcio G, Casagrande M, Bertini M (2001) Sleepiness: evaluating and quantifying methods. Int J Psychophysiol 41:251–263PubMedCrossRefGoogle Scholar
  6. 6.
    Dinges DF, Pack F, Williams K, Gillen KA,Powell JW,Ott GE,Aptowicz C,Pack AI (1997) Cumulative sleepiness,mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restricted to 4–5 hours per night. Sleep 20:267–277PubMedGoogle Scholar
  7. 7.
    Drapeau C, Carrier J (2004) Fluctuation of waking electroencephalogram and subjective alertness during a 25-hour sleep-deprivation episode in young and middle-aged subjects. Sleep 27:55–60PubMedGoogle Scholar
  8. 8.
    Finelli LA, Baumann H, Borbely AA, Achermann P (2000) Dual electroencephalogram markers of human sleep homeostasis: correlation between theta activity in waking and slow-wave activity in sleep. Neuroscience 101:523–529PubMedCrossRefGoogle Scholar
  9. 9.
    Fingelkurts AnA, Fingelkurts AlA, Krause CM, Kaplan AYa, Borisov SV, Sams M (2003) Structural (operational) synchrony of EEG alpha activity during an auditory memory task. NeuroImage 20:529–542PubMedCrossRefGoogle Scholar
  10. 10.
    Gillberg M, Kecklund G, Åkerstedt T (1994) Relation between performance and subjective ratings of sleepiness during a night awake. Sleep 17:236–241PubMedGoogle Scholar
  11. 11.
    Kaplan AYa, Fingelkurts AnA, Fingelkurts AlA, Borisov SV, Darkhovsky BS (2005) Nonstationary nature of the brain activity as revealed by EEG/MEG: methodological, practical and conceptual challenges. Signal Processing 85:2190–2212CrossRefGoogle Scholar
  12. 12.
    Kaplan AY, Shishkin SL (2000) Application of the change-point analysis to the investigation of the brain’s electrical activity. In: Brodsky BE, Darkhovsky BS (eds) Nonparametric Statistical Diagnosis: Problems and Methods. Dordrecht (the Netherlands): Kluwer Academic Publishers, pp 333–388Google Scholar
  13. 13.
    Latta F, Leproult R, Tasali E, Hofmann E, Van Cauter E (2005) Sex differences in delta and alpha EEG activities in healthy older adults. Sleep 28:1525–1534PubMedGoogle Scholar
  14. 14.
    Leproult R, Colecchoa EF, Berardi AM, Stickgold R, Kosslyn SM, Van Cauter E (2003) Individual differences in subjective and objective alertness during sleep deprivation are stable and unrelated. Am J Physiol Regulatory Integrative Comp Physiol 284:R280–R290Google Scholar
  15. 15.
    Lorenzo I, Ramos J, Arce C, Guevara MA, Corsi-Cabrera M (1995) Effect of total sleep deprivation on reaction time and waking EEG activity in man. Sleep 18:346–354PubMedGoogle Scholar
  16. 16.
    Mitler MM, Carskadon MA, Czeisler CA, Dement WC, Dinges DF, Graeber RC (1988) Catastrophes, sleep, and public policy: consensus report. Sleep 11:100–109PubMedGoogle Scholar
  17. 17.
    Nunez PL (2000) Toward a quantitative description of large scale neocortical dynamic function and EEG. Behav Brain Sci 23:371–437PubMedCrossRefGoogle Scholar
  18. 18.
    Oken BS, Chiappa KH (1988) Shortterm variability in EEG frequency analysis. Electroencephalogr Clin Neurophysiol 69:191–198PubMedCrossRefGoogle Scholar
  19. 19.
    Putilov AA (2007) Introduction of the tetra-circumplex criterion for comparison of the actual and theoretical structures of the sleep-wake adaptability. Biol Rhythm Res 38:65–84CrossRefGoogle Scholar
  20. 20.
    Putilov AA, Putilov DA (2005) Sleepless in Siberia and Alaska: cross-validation of factor structure of the individual adaptability of the sleep-wake cycle. Ergonomia 27:207–226Google Scholar
  21. 21.
    Putilov AA,Putilov DA (2006) Big six of the individual adaptive ability of the sleep-wake cycle: explanation and measurement. Biol Rhythm Res 37:51–71CrossRefGoogle Scholar
  22. 22.
    Risser MR,Ware JC,Freeman FG (2000) Driving simulation with EEG monitoring in normal and obstructive sleep apnea patients. Sleep 23:393–398PubMedGoogle Scholar
  23. 23.
    Santamaria J, Chiappa KH (1987) The EEG of drowsiness in normal adults. J Clin Neurophysiol 4:327–382PubMedCrossRefGoogle Scholar
  24. 24.
    Sforza E, Grandin S, Jouny C, Rochat T, Ibanez V (2002) Eur Respir J 19:645–652PubMedCrossRefGoogle Scholar
  25. 25.
    Stampi C,Stone P,Michimori A (1995) A new quantitative method for assessing sleepiness: The alpha attenuation test. Work and Stress 9:368–376Google Scholar
  26. 26.
    Strijkstra AM, Beersma DG, Drayer B, Halbesma N, Daan S (2003) Subjective sleepiness correlates negatively with global alpha (8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awake electroencephalogram. Neurosci Lett 340:17–20PubMedCrossRefGoogle Scholar
  27. 27.
    Taillard J, Moore N, Claustrat B, Coste O, Bioulac B, Philip P (2006) Nocturnal sustained attention during sleep deprivation can be predicted by specific periods of subjective daytime alertness in normal young humans. J Sleep Res 15:41–45PubMedCrossRefGoogle Scholar
  28. 28.
    Truccolo WA,Ding M,Knuth KH,Nakamura R, Bressler S (2002) Trial-to-trial variability of cortical evoked responses: Implications for analysis of functional connectivity. Clin Neurophysiol 113:206–226PubMedCrossRefGoogle Scholar

Copyright information

© Steinkopff-Verlag 2007

Authors and Affiliations

  • D. A. Putilov
    • 1
  • E. G. Verevkin
    • 1
  • O. G. Donskaya
    • 1
  • A. A. Putilov
    • 2
  1. 1.Research Institute for Molecular Biology and BiophysicsSiberian Branch of the Russian Academy of Medical SciencesNovosibirskRussia
  2. 2.BerlinGermany

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