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A novel system for automatic detection of K-complexes in sleep EEG

  • New Trends in data pre-processing methods for signal and image classification
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Abstract

Sleep staging process is applied to diagnose sleep-related disorders by sleep experts through analyzing sleep signals such as electroencephalogram (EEG), electrooculogram and electromyogram of subjects and determining the stages in 30-s-length time parts of sleep named as epochs. Subjects enter several stages during the sleep, and N-REM2 is one of them which has also the highest duration among the other stages. Approximately half of the sleep consists of N-REM2. One of the important parameters in determining N-REM2 stage is K-complex (Kc). In this study, some time and frequency analysis methods were used to determine the locations of Kcs, automatically. These are singular value decomposition (SVD), variational mode decomposition and discrete wavelet transform. The performance of them in detecting Kcs was compared. Furthermore, systems with combinations of these methods were presented with logic AND operations. The EEG recordings of seven subjects were obtained from the Sleep Research Laboratory of Necmettin Erbakan University. A database with total 359 Kcs in 320 epochs was prepared from the recordings. According to the results, the highest average recognition rate was found as 92.29% for the SVD method. Thanks to this study, the sleep experts can find out whether there were Kcs in related epochs and also know their locations in these epochs, automatically. Also, it will help automatic sleep stage classification systems.

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References

  1. Rechtschaffen A, Kales A (1969) A manual of standardized terminology. Techniques and scoring system for sleep stages of human subjects. US Government Printing Office, Washington

    Google Scholar 

  2. Flemons WW, Buysse D, Redline S, Pack A, Strohl K, Wheatley J, Young T, Douglas N, Levy P, McNicholas W, Fleetham J, White D, Schmidt-Nowarra W, Carley D, Romaniuk J, Force AASMT (1999) Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Sleep 22(5):667–689

    Article  Google Scholar 

  3. Camilleri TA, Camilleri KP, Fabri SG (2014) Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models. Biomed Signal Process Control 10:117–127

    Article  Google Scholar 

  4. Bankman IN, Sigillito VG, Wise RA, Smith PL (1992) Feature-based detection of the K-complex wave in the human electroencephalogram using neural networks. IEEE Trans Biomed Eng 39(12):1305–1310

    Article  Google Scholar 

  5. Richard C, Lengelle R (1998) Joint time and time-frequency optimal detection of K-complexes in sleep EEG. Comput Biomed Res 31(3):209–229

    Article  Google Scholar 

  6. Jansen BH, Desai PR (1994) K-complex detection using multi-layer perceptrons and recurrent networks. Int J Biomed Comput 37(3):249–257. doi:10.1016/0020-7101(94)90123-6

    Article  Google Scholar 

  7. Bremer G, Smith JR, Karacan I (1970) Automatic detection of the K-complex in sleep electroencephalograms. IEEE Trans Biomed Eng 17(4):314–323

    Article  Google Scholar 

  8. Jansen BH, Dawant BM, Meddahi K, Martens W, Griep P, Declecrk AC (1989) AI techniques for K-complex detection in human sleep EEGs. In: Proceedings of the annual international conference of the ieee engineering in engineering in medicine and biology society, 1989. Images of the twenty-first century. IEEE, pp 1806–1807

  9. Henry D, Sauter D, Caspary O (1994) Comparison of detection methods: application to K-complex detection in sleep EEG. In: Engineering in medicine and biology society. Engineering advances: new opportunities for biomedical engineers. Proceedings of the 16th annual international conference of the IEEE, vol 1212, pp 1218–1219. doi:10.1109/IEMBS.1994.415401

  10. Kam A, Cohen A, Geva AB, Tarasiuk A (2004) Detection of K-complexes in sleep EEG using CD-HMM. In: Engineering in medicine and biology society. IEMBS ‘04. 26th Annual international conference of the IEEE, 1–5 Sept. 2004, pp 33–36. doi:10.1109/IEMBS.2004.1403083

  11. Erdamar A, Duman F, Yetkin S (2012) A wavelet and teager energy operator based method for automatic detection of K-complex in sleep EEG. Expert Syst Appl 39(1):1284–1290

    Article  Google Scholar 

  12. Devuyst S, Dutoit T, Stenuit P, Kerkhofs M (2010) Automatic K-complexes detection in sleep EEG recordings using likelihood thresholds. In: 2010 Annual international conference of the ieee engineering in medicine and biology, Aug. 31 2010–Sept. 4 2010, pp 4658–4661. doi:10.1109/IEMBS.2010.5626447

  13. Zacharaki EI, Pippa E, Koupparis A, Kokkinos V, Kostopoulos GK, Megalooikonomou V (2013) One-class classification of temporal EEG patterns for K-complex extraction. Conf Proc IEEE Eng Med Biol Soc 2013:5801–5804. doi:10.1109/EMBC.2013.6610870

    Google Scholar 

  14. Krohne LK, Hansen RB, Christensen JA, Sorensen HB, Jennum P (2014) Detection of K-complexes based on the wavelet transform. Conf Proc IEEE Eng Med Biol Soc 2014:5450–5453. doi:10.1109/EMBC.2014.6944859

    Google Scholar 

  15. Noori SMR, Hekmatmanesh A, Mikaeili M, Sadeghniiat-Haghighi K (2014) K-complex identification in sleep EEG using MELM-GRBF classifier. In: 2014 21th Iranian conference on biomedical engineering (ICBME), 26–28 Nov 2014, pp 119–123. doi:10.1109/ICBME.2014.7043905

  16. Zamir ZR, Sukhorukova N, Amiel H, Ugon A, Philippe C (2015) Convex optimisation-based methods for K-complex detection. Appl Math Comput 268:947–956

    MathSciNet  Google Scholar 

  17. Parekh A, Selesnick IW, Rapoport DM, Ayappa I (2015) Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization. J Neurosci Methods 251:37–46

    Article  Google Scholar 

  18. Hernandez-Pereira E, Bolon-Canedo V, Sanchez-Marono N, Alvarez-Estevez D, Moret-Bonillo V, Alonso-Betanzos A (2016) A comparison of performance of K-complex classification methods using feature selection. Inf Sci 328:1–14

    Article  Google Scholar 

  19. Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 32(3):230–236

    Article  Google Scholar 

  20. Lee S, Hayes MH (2004) Properties of the singular value decomposition for efficient data clustering. IEEE Signal Proc Lett 11(11):862–866

    Article  Google Scholar 

  21. Xia Y, Zhou W, Li C, Yuan Q, Geng S (2015) Seizure detection approach using S-transform and singular value decomposition. Epilepsy Behav 52:187–193

    Article  Google Scholar 

  22. Hassanpour H, Mesbah M, Boashash B (2004) Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques. EURASIP J Adv Signal Process 16:1–11. doi:10.1155/s1110865704406167

    MATH  Google Scholar 

  23. Huang NE, Shen Z, Long SR, Wu MLC, Shih HH, Zheng QN, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys 454(1971):903–995

    Article  MathSciNet  MATH  Google Scholar 

  24. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

    Article  MathSciNet  Google Scholar 

  25. Liu YY, Yang GL, Li M, Yin HL (2016) Variational mode decomposition denoising combined the detrended fluctuation analysis. Signal Process 125:349–364

    Article  Google Scholar 

  26. Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027–2036

    Article  Google Scholar 

  27. Mallat SG (1989) A theory for multiresolution signal decomposition—the wavelet representation. IEEE Trans Pattern Anal 11(7):674–693

    Article  MATH  Google Scholar 

  28. Ullah AMMS (2010) A DNA-based computing method for solving control chart pattern recognition problems. CIRP J Manuf Sci Technol 3(4):293–303. doi:10.1016/j.cirpj.2011.02.002

    Article  Google Scholar 

  29. Ullah AMMS, Harib KH (2010) Simulation of cutting force using nonstationary Gaussian process. J Intell Manuf 21(6):681–691. doi:10.1007/s10845-009-0245-2

    Article  Google Scholar 

  30. Ullah A, Harib KH (2006) Knowledge extraction from time series and its application to surface roughness simulation. Inf Knowl Syst Manag 5(2):117–134

    Article  Google Scholar 

Download references

Acknowledgements

This study is supported by the Scientific and Technological Research Council of Turkey (project no. 113E591) and the Scientific Research Projects Coordination Unit of Selcuk University.

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Selcuk University, 42072, Konya, Turkey

    Cüneyt Yücelbaş & Seral Özşen

  2. Computer Engineering Department, Selcuk University, Konya, Turkey

    Şule Yücelbaş & Gülay Tezel

  3. Sleep Laboratory, Faculty of Medicine, Necmettin Erbakan University, Konya, Turkey

    Serkan Küççüktürk & Şebnem Yosunkaya

Authors
  1. Cüneyt Yücelbaş
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  2. Şule Yücelbaş
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  3. Seral Özşen
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  4. Gülay Tezel
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  5. Serkan Küççüktürk
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  6. Şebnem Yosunkaya
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Correspondence to Cüneyt Yücelbaş.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national Non-invasive Clinical Research Medical Ethics Review Board and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Proper informed consent was obtained from all individual participants included in the study.

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Yücelbaş, C., Yücelbaş, Ş., Özşen, S. et al. A novel system for automatic detection of K-complexes in sleep EEG. Neural Comput & Applic 29, 137–157 (2018). https://doi.org/10.1007/s00521-017-2865-3

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