Abstract
Segmentation is a crucial part of the signal processing as it has a significant influence on further analysis quality. Adaptive segmentation based on sliding windows is relatively simple, works quite good and can work online. It has however many tunable parameters whose proper values depend on the task and signal type. The paper proposes a method of defining optimal parameters for detection of sleep spindles in electroencephalogram. Segmentation algorithm based on Varri method was utilized. Fitness function was proposed for estimation of agreement between the segmentation result and borders of the target classification. Particle swarm optimization was used to find optimal parameters. On the data of 11 insomniac subjects the method reached \(28\,\%\) improvement in comparison to the baseline method using default parameters.
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References
Acr, N., Gzeli, C.: Automatic recognition of sleep spindles in EEG by using artificial neural networks. Expert Syst. Appl. 27(3), 451–458 (2004)
Agarwal, R., Gotman, J.: Adaptive segmentation of electroencephalographic data using a nonlinear energy operator. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS 1999), Orlando, FL, vol. 4, pp. 199–202 (1999)
Anisheh, S.M., Hassanpour, H.: Adaptive segmentation with optimal window length scheme using fractal dimension and wavelet transform. IJE Trans. B: Appl. 22(3), 257–268 (2009)
Appel, U., Brandt, A.: A comparative study of three sequential time series segmentation algorithms. Sig. Process. 6(1), 45–60 (1984)
Esteller, R.: A comparison of waveform fractal dimension algorithms. Circuits Syst. I: Fundam. Theory Appl. 48(2), 177–183 (2001)
Ferrarelli, F., et al.: Reduced sleep spindle activity in schizophrenia patients. Am. J. Psychiatry 164(3), 483–492 (2007)
Gennaro, L.D., Ferrara, M.: Sleep spindles: an overview. Sleep Med. Rev. 7(5), 423–440 (2003)
Gensler, A., Sick, B.: Novel criteria to measure performance of time series segmentation techniques. In: LWA (2014)
Gunes, S., Dursun, M., Polat, K., Yosunkaya, S.: Sleep spindles recognition system based on time and frequency domain features. Expert Syst. Appl. 38(3), 2455–2461 (2011)
Kaiser, J.F.: On a simple algorithm to calculate the ‘energy’, of a signal. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, NM, vol. 1, pp. 381–384 (1990)
Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE International Conference on Neural Networks 1995. Proceedings, vol. 4, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Koley, B., Dey, D.: An ensemble system for automatic sleep stage classification using single channel eeg signal. Comput. Biol. Med. 42(12), 1186–1195 (2012)
Krajca, V., Petranek, S., Patakova, I., Varri, A.: Automatic identificaton of significant graphoelements in multichannel eeg recordings by adaptive segmentation and fuzzy clustering. Int. J. Biomed. Comput. 28, 71–89 (1991)
Limoges, E., Mottron, L., Bolduc, C., Berthiaume, C., Godbout, R.: Atypical sleep architecture and the autism phenotype. Brain: J. Neurol. 128(5), 1049–1061 (2005)
Myatchin, I., Lagae, L.: Sleep spindle abnormalities in children with generalized spike-wave discharges. Pediatr. Neurol. 36(2), 106–111 (2007)
Rechtschaffen, A., Kales, A.: A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Clin. Neurophysiol. 26(6), 644 (1968)
Sinha, R.K.: Artificial neural network and wavelet based automated detection of sleep spindles, rem sleep and wake states. J. Med. Syst. 32(4), 291–299 (2008)
Varri, A.: Digital Processing of the EEG in Epilepsy. Ph.D. Thesis, Tampere University of Technology, Tampere, Finland (1988)
Wamsley, E.J., et al.: Reduced sleep spindles and spindle coherence in schizophrenia: mechanisms of impaired memory consolidation? Biol. Psychiatry 71(2), 154–161 (2012)
Zoubek, L., Charbonnier, S., Lesecq, S., Buguet, A., Chapotot, F.: Feature selection for sleep/wake stages classification using data driven methods. Biomed. Sig. Process. Control 2(3), 171–179 (2007)
Acknowledgments
Research of E. Saifutdinova was supported by the project No. SGS16/231/ OHK3/3T/13 of the Czech Technical University in Prague. Research of Martin Macas was supported by the project No. GP13-21696P “Feature selection for temporal context aware models of multivariate time series” of the Grant Agency of the Czech Republic (GACR). This publication was supported by the project “National Institute of Mental Health (NIMH-CZ)”, grant number CZ.1.05/2.1.00/03.0078 and the European Regional Development Fund.
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Saifutdinova, E., Macaš, M., Gerla, V., Lhotská, L. (2016). Adaptive Segmentation Optimization for Sleep Spindle Detector. In: Renda, M., Bursa, M., Holzinger, A., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2016. Lecture Notes in Computer Science(), vol 9832. Springer, Cham. https://doi.org/10.1007/978-3-319-43949-5_6
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