Research Article

Determination of sleep spindles with time series approach

Volume: 67 Number: 2 December 24, 2025

Determination of sleep spindles with time series approach

Abstract

The graphoelements of electroencephalography (EEG), which form the sleep structure, are one of the important research areas of electrophysiology and cognitive science. Sleep structuring is done by experienced sleep experts according to standardized rules. It is performed by visual evaluation according to the graphoelements in the EEG recorded during the night's sleep. This process is subjective, requires time and susceptible for human errors. Among these graphoelements, sleep spindles are the characteristic EEG wave activities. They belong to the non-rapid eye movement (NREM) N2 sleep period. They play a role in sleep structure and cognitive functions. Therefore, it has recently been the subject of research as a clinical determining factor in neuropsychiatric disorders. The initial stage of detection and then further analysis of the waveforms of sleep spindles and the determination of the time and duration of the spindles have gained importance in research. However, since sleep spindles have transient properties compared to background EEG signals, they are difficult to visually analyze. In this study, the sleep EEG was modeled with the time-varying parameter Autoregressive AR (9) to determine the sleep spindles encountered in the sleep EEG. In dynamic linear models given with AR (9), the parameter vector is accepted as a random walk process. Therefore, it can be written as a state space model. By using the adaptive Kalman filter (AKF), the model parameters that can be easily estimated are the time variable parameter and the variance of the white noise process. The focal point of our analysis revealed that the estimated values showed significant changes in the parts with the spindles. We therefore propose that the estimated values of the time varying variance of the white noiseprocess will provide a valuable decision support to sleep experts working in the field of sleep scoring. It can be considered that the method discussed in our study can be combined with existing automatic scoring algorithms and can also be used as a control mechanism in manual scoring for inexperienced sleep scorers.

Keywords

References

  1. Berger, H., Ueber das elektroenkephalogramm des Menschen, J. Psychol. Neurol., 40 (1930), 160-179.
  2. Rechtschaffen, A., Kales, A., Brain Information Service, and Brain Research Institute UoC, A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, US Government Printing Office, US Public Health Service, 1968.
  3. Yücelbaş, C., Yücelbaş, Ş., Özşen, S., et al., Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods, Neural Comput. Applic., 29 (2018),17-33, https://doi.org/10.1007/s00521-016-2445-y.
  4. Olbrich, E., Achermann, P., Oscillatory events in the human sleep EEG- detection and properties, Neurocomputing, 58 (2004), 129-135, https://doi.org/10.1016/j.neucom. 2004.01.033.
  5. Kaplan, A., Röschke, J., Darkhovsky, B., Fell, J., Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis, J. Neurosci. Methods, 106 (2001), 81-90, https://doi.org/10.1016/s0165-0270(01)00331-4.
  6. Olbrich, E., Achermann, P., Meier, P. F., Dynamics of human sleep EEG, Neurocomputing, 52 (2003), 857-862, https://doi.org/10.1016/S0925-2312(02)00816-0.
  7. Pardey, J., Robert, S., Tarassenko, L. A., Review of parametric modelling techniques for EEG analysis, Med. Eng. Phys., 18 (1) (1995), 2-11, https://doi.org/10.1016/13504533(95)00024-0.
  8. Perumalsamy, V., Sankaranarayanan, S., Rajamony, S., Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: A surrogate data approach, J. Biomed. Sci. Eng., 2 (2009), 294-303, https://doi.org/10.4236/jbise.2009.25044.

Details

Primary Language

English

Subjects

Biomedical Diagnosis

Journal Section

Research Article

Publication Date

December 24, 2025

Submission Date

January 4, 2025

Acceptance Date

April 24, 2025

Published in Issue

Year 2025 Volume: 67 Number: 2

APA
Özbek, L., Yetkin, S., Zupan, A., İlk, H. G., & Ceran Gürlek, N. N. (2025). Determination of sleep spindles with time series approach. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 67(2), 107-124. https://doi.org/10.33769/aupse.1613459
AMA
1.Özbek L, Yetkin S, Zupan A, İlk HG, Ceran Gürlek NN. Determination of sleep spindles with time series approach. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2025;67(2):107-124. doi:10.33769/aupse.1613459
Chicago
Özbek, Levent, Sinan Yetkin, Asuhan Zupan, Hakkı Gökhan İlk, and Nihan Nur Ceran Gürlek. 2025. “Determination of Sleep Spindles With Time Series Approach”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 67 (2): 107-24. https://doi.org/10.33769/aupse.1613459.
EndNote
Özbek L, Yetkin S, Zupan A, İlk HG, Ceran Gürlek NN (December 1, 2025) Determination of sleep spindles with time series approach. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 67 2 107–124.
IEEE
[1]L. Özbek, S. Yetkin, A. Zupan, H. G. İlk, and N. N. Ceran Gürlek, “Determination of sleep spindles with time series approach”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 67, no. 2, pp. 107–124, Dec. 2025, doi: 10.33769/aupse.1613459.
ISNAD
Özbek, Levent - Yetkin, Sinan - Zupan, Asuhan - İlk, Hakkı Gökhan - Ceran Gürlek, Nihan Nur. “Determination of Sleep Spindles With Time Series Approach”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 67/2 (December 1, 2025): 107-124. https://doi.org/10.33769/aupse.1613459.
JAMA
1.Özbek L, Yetkin S, Zupan A, İlk HG, Ceran Gürlek NN. Determination of sleep spindles with time series approach. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2025;67:107–124.
MLA
Özbek, Levent, et al. “Determination of Sleep Spindles With Time Series Approach”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 67, no. 2, Dec. 2025, pp. 107-24, doi:10.33769/aupse.1613459.
Vancouver
1.Levent Özbek, Sinan Yetkin, Asuhan Zupan, Hakkı Gökhan İlk, Nihan Nur Ceran Gürlek. Determination of sleep spindles with time series approach. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2025 Dec. 1;67(2):107-24. doi:10.33769/aupse.1613459

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License