Abstract
In this paper we present a combined SVM-HMM sleep spindle detection scheme. The proposed scheme takes advantage of the information provided from each of the two prediction models in decision level, in order to provide refined and more accurate spindle detection results. The experimental results showed that the proposed combined scheme achieved an overall detection performance of 90.28%, increasing the best-performing SVM-based model by 2% in terms of absolute performance.
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References
Sanei, S., Chambers, J.A.: EEG Signal Processing. John Wiley & Sons, Ltd. (2007)
De Gennaro, L., Ferrara, M.: Sleep spindles: an overview. Sleep Medicine Reviews 7(5), 423–440 (2003)
D. Gorur, U. Halici, H. Aydin, G. Ongun, F. Ozgen, K. Leblebicioglu, "Sleep Spindles Detection Using Autoregressive Modeling", In Proc. of ICANN/ICONIP 2003.
Huupponen, E., Gomez-Herrero, G., Saastamoinen, A., Varri, A., Hasan, J., Himanen, S.-L.: Development and comparison of four sleep spindle detection methods. Artificial Intelligence in Medicine 40, 157–170 (2007)
De Gennaro, L., Ferrara, M., Bertini, M.: Effect of slow-wave sleep deprivation on topographical distribution of spindles. Behavioral Brain Research (116), 55–59 (2000)
Ktonas, P.Y., Golemati, S., Xanthopoulos, P., Sakkalis, V., Ortigueira, M.D.: Time–frequency analysis methods to quantify the time-varying microstructure of sleep EEG spindles: possibility for dementia biomarkers. Journal of Neuroscience Methods, no 1(185), 133–142 (2009)
Ahmed, B., Redissi, A., Tafreshi, R.: An automatic sleep spindle detector based on wavelets and the teager energy operator. In: Proc. of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2596–2599 (2009)
Duman, F., Erogul, O., Telatar, Z., Yetkin, S.: Automatic sleep spindle detection and localization algorithm. In: Proc. of EUSIPCO 2005, pp. 2003–2006 (2005)
Devuyst, S., Dutoit, T., Didier, J.F., Meers, F., Stanus, E., Stenuit, P., Kerkhofs, M.: Automatic Sleep Spindle Detection in Patients with Sleep Disorders. In: Proc. of the 28th IEEE EMBS Annual International Conference, New York City, USA (2006)
Ventouras, E., Monoyiou, E., Ktonas, P., Paparrigopoulos, T., Dikeos, D.: Sleep Spindle Detection Using Artificial Neural Networks Trained with Filtered Time-Domain EEG: A Feasibility Study. Computer Methods and Programs in Biomedicine 78(3), 191–207 (2005)
Ventouras, E., Economou, N., Kritikou, I., Tsekou, H., Paparrigopoulos, T., Ktonas, P.: Performance evaluation of an Artificial Neural Network automatic spindle detection system. In: Proc. of the Conf. IEEE Eng. Med. Biol. Soc., pp. 4328–4331 (2012)
Duman, F., Erdamar, A., Erogul, O., Telatar, Z., Yetkin, S.: Efficient sleep spindle detection algorithm with decision tree. Expert Systems with Applications 36(6), 9980–9985 (2009)
Costa, J., Ortigueira, M., Batista, A., Paiva, T.: Sleep Spindles Detection: a Mixed Method using STFT and WMSD. International Journal of Bioelectromagnetism 14(4), 229–233 (2012)
Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Computation 13(3), 637–649 (2001)
Rabiner, L.R.: A tutorial on Hidden Markov Models and Selected Applications in Speech recognition. Proceedings of the IEEE 77(2) (1989)
Witten, H.I., Frank, E.: Data Mining: practical machine learning tools and techniques. Morgan Kaufmann Publishing
Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK Book (for HTK Version 3.4). Cambridge University Engineering Department (2006)
Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains. Annals of Mathematical Statistics 41(1), 164–171 (1970)
Ventouras, E.M., Ktonas, P.Y., Tsekou, H., Paparrigopoulos, T., Kalatzis, I., Soldatos, C.R.: Independent Component Analysis for Source Localization of EEG Sleep Spindle Components. In: Computational Intelligence and Neuroscience, vol. 2010 (2010)
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Mporas, I., Korvesis, P., Zacharaki, E.I., Megalooikonomou, V. (2013). Sleep Spindle Detection in EEG Signals Combining HMMs and SVMs. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41016-1_15
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DOI: https://doi.org/10.1007/978-3-642-41016-1_15
Publisher Name: Springer, Berlin, Heidelberg
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