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Improvement of Sleep Spindle Detection by Aggregation Techniques

  • Elizaveta SaifutdinovaEmail author
  • Daniela Dudysova
  • Vaclav Gerla
  • Lenka Lhotska
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

The study focuses on automatic sleep spindle detection. Plenty of methods have been proposed in previous decades. However, there is still space for improvement. In this study, we investigate aggregation methods such as voting to achieve better results. We employ an unweighted model and two weighted voting models in which assigned weights represent reliability of automatic detectors. First weighted model utilizes supervised approach based on logistic regression. The second one applies unsupervised generative Bayesian model often used in crowdsourcing. Using the expectation maximization algorithm, we uncover hidden true labels and weighs of detectors. We test methods on the real world datasets. The aggregation method overcome single detectors on 10% on average in terms of F1. Moreover, a probabilistic explanation of weights could be used in applications for visual analysis.

Keywords

Human sleep EEG Sleep spindle Aggregation Unsupervised methods Supervised methods 

Notes

Acknowledgment

Research was supported by the project Temporal context in analysis of long-term non-stationary multidimensional signal, Register Number 17-20480S of the Grant Agency of the Czech Republic. This work was also supported by the Charles University research program PROGRES Q35, by the project No. LO1611 with financial support from the MEYS under the NPU I program and by the Ministry of Health of the Czech Republic, grant No. NV18-07-00272. All rights reserved.

References

  1. 1.
    Ahmed, B., Redissi, A., Tafreshi, R.: An automatic sleep spindle detector based on wavelets and the teager energy operator. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2596–2599, September 2009Google Scholar
  2. 2.
    Born, J., Rasch, B., Gais, S.: Sleep to remember. Neuroscientist 12(5), 410–424 (2006).  https://doi.org/10.1177/1073858406292647. pMID: 16957003CrossRefGoogle Scholar
  3. 3.
    Devuyst, S., Dutoit, T., Stenuit, P., Kerkhofs, M.: Automatic sleep spindles detection – overview and development of a standard proposal assessment method. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1713–1716, August 2011Google Scholar
  4. 4.
    Duman, F., Erdamar, A., Erogul, O., Telatar, Z., Yetkin, S.: Efficient sleep spindle detection algorithm with decision tree. Expert Syst. Appl. 36(6), 9980–9985 (2009). http://www.sciencedirect.com/science/article/pii/S0957417409001055CrossRefGoogle Scholar
  5. 5.
    Gais, S., Mölle, M., Helms, K., Born, J.: Learning-dependent increases in sleep spindle density. J. Neurosci. 22(15), 6830–6834 (2002). http://www.jneurosci.org/content/22/15/6830CrossRefGoogle Scholar
  6. 6.
    Gennaro, L.D., Ferrara, M.: Sleep spindles: an overview. Sleep Med. Rev. 7(5), 423–440 (2003). http://www.sciencedirect.com/science/article/pii/S1087079202902522CrossRefGoogle Scholar
  7. 7.
    Huupponen, E., Gomez-Herrero, G., Saastamoinen, A., Värri, A., Hasan, J., Himanen, S.L.: Development and comparison of four sleep spindle detection methods. Artif. Intell. Med. 40(3), 157–170 (2007). http://www.sciencedirect.com/science/article/pii/S0933365707000516CrossRefGoogle Scholar
  8. 8.
    Kantchelian, A., Tschantz, M.C., Afroz, S., Miller, B., Shankar, V., Bachwani, R., Joseph, A.D., Tygar, J.D.: Better malware ground truth: techniques for weighting anti-virus vendor labels. In: Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, AISec 2015, pp. 45–56. ACM, New York (2015). http://doi.acm.org/10.1145/2808769.2808780
  9. 9.
    Lacourse, K., Delfrate, J., Beaudry, J., Peppard, P., Warby, S.C.: A sleep spindle detection algorithm that emulates human expert spindle scoring. J. Neurosc. Methods 316, 3–11 (2019). http://www.sciencedirect.com/science/article/pii/S0165027018302504, methods and models in sleep research: A Tribute to Vincenzo CrunelliCrossRefGoogle Scholar
  10. 10.
    Liu, M.Y., Huang, A., Huang, N.E.: Evaluating and improving automatic sleep spindle detection by using multi-objective evolutionary algorithms. Front. Hum. Neurosci. 11, 261 (2017). https://www.frontiersin.org/article/10.3389/fnhum.2017.00261CrossRefGoogle Scholar
  11. 11.
    Martin, N., Lafortune, M., Godbout, J., Barakat, M., Robillard, R., Poirier, G., Bastien, C., Carrier, J.: Topography of age-related changes in sleep spindles. Neurobiol. Aging 34(2), 468–476 (2013). http://www.sciencedirect.com/science/article/pii/S019745801200320XCrossRefGoogle Scholar
  12. 12.
    Mölle, M., Marshall, L., Gais, S., Born, J.: Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep. J. Neurosci. 22(24), 10941–10947 (2002). http://www.jneurosci.org/content/22/24/10941CrossRefGoogle Scholar
  13. 13.
    O’Reilly, C., Nielsen, T.: Automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools. Front. Hum. Neurosci. 9, 353 (2015). https://www.frontiersin.org/article/10.3389/fnhum.2015.00353Google Scholar
  14. 14.
    Parekh, A., Selesnick, I.W., Osorio, R.S., Varga, A.W., Rapoport, D.M., Ayappa, I.: Multichannel sleep spindle detection using sparse low-rank optimization. J. Neurosci. Methods 288, 1–16 (2017). http://www.sciencedirect.com/science/article/pii/S0165027017301681CrossRefGoogle Scholar
  15. 15.
    Raykar, V.C., Yu, S., Zhao, L.H., Jerebko, A., Florin, C., Valadez, G.H., Bogoni, L., Moy, L.: Supervised learning from multiple experts: Whom to trust when everyone lies a bit. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 889–896. ACM, New York (2009). http://doi.acm.org/10.1145/1553374.1553488
  16. 16.
    Reynolds, C., Short, M., Gradisar, M.: Sleep spindles and cognitive performance across adolescence: a meta-analytic review. J. Adolesc. 66, 55–70 (2018). http://www.sciencedirect.com/science/article/pii/S0140197118300599CrossRefGoogle Scholar
  17. 17.
    Wamsley, E.J., Tucker, M.A., Shinn, A.K., Ono, K.E., McKinley, S.K., Ely, A.V., Goff, D.C., Stickgold, R., Manoach, D.S.: Reduced sleep spindles and spindle coherence in schizophrenia: mechanisms of impaired memory consolidation? Bio. Psychiatr. 71(2), 154–161 (2012). http://www.sciencedirect.com/science/article/pii/S0006322311008146, functional Consequences of Altered Cortical Development in SchizophreniaCrossRefGoogle Scholar
  18. 18.
    Warby, S.C., Wendt, S.L., Welinder, P., Munk, E.G., Carrillo, O., Sorensen, H.B.D., Jennum, P., Peppard, P.E., Perona, P., Mignot, E.: Sleep spindle detection: crowdsourcing and evaluating performance of experts, non-experts, and automated methods. In: Nature Methods (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Elizaveta Saifutdinova
    • 1
    • 2
    Email author
  • Daniela Dudysova
    • 1
  • Vaclav Gerla
    • 2
  • Lenka Lhotska
    • 2
  1. 1.National Institute of Mental HealthKlecanyCzech Republic
  2. 2.Czech Technical University in PraguePragueCzech Republic

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