睡眠纺锤波

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请问各位大侠，用什么软件自动判读、分析睡眠纺锤波？

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有很多方式：Artificial Neural Networks (ANN) [10] E. Ventouras, E. Monoyiou, P. Ktonas, T. Paparrigopoulos, D. Dikeos, N. Uzunoglu, C. Soldatos, “Sleep Spindle Detection Using Artificial Neural Networks Trained with Filtered Time-Domain EEG: A Feasibility Study,” Comput. Meth. Prog. Bio., vol. 78, pp. 191-207, 2005. [15] N. Acır, and C. Gόzelis “Automatic recognition of sleep spindles in EEG by using artificial neural networks,” Expert Syst. Appl., vol. 27, no. 3, pp. 451–458, 2004. [19] Gunes, S., Dursun, M., Polat, K., Yosunkaya, S., “Sleep spindles recognition system based on time and frequency domain features,” Expert Systems with Applications, 38, 2455–2461, 2011 comprise frequency and amplitude **ysis  [12] P. Schimicek, J. Zeitlhofer, P. Anderer, B. Saletu, “Automatic sleep spindle detection procedure: aspects of reliability and validity,” Clin. Electroencephal., vol. 25, no. 1, pp. 26-29, 1994. [13] E. Huupponen, G. Gomez-Herrero, A. Saastamoinen, A. Varri, J. Hasan, S.-L. Himanen, “Development and comparison of four sleep spindle detection methods,” Artif. Intell. Med., vol. 40, pp. 157-170, 2007. [14] R. Bódizs, J. Körmendi, P. Rigó, A. Sándor Lázár,“The individual adjustment method of sleep spindle **ysis: methodological improvements and roots in the fingerprint paradigm,” J. Neurosci. Meth., vol. 178, no. 1, pp. 205-213, 2009.  [25] A. Nonclercq, C. Urbain, D. Verheulpen, C. Decaestecker, P. Van Bogaert, P. Peigneux, “Sleep spindle detection through amplitude– frequency normal modelling,” J. Neurosci. Meth., vol. 214, no. 2, pp. 192-203, 2013. Fuzzy detectors [11] L. Causa, C. Held, J. Causa, P. Estévez, C. Perez, R. Chamorro, M. Garrido, C. Algarín, P. Peirano, “Automated sleep-spindle detection in healthy children polysomnograms,” IEEE Trans. Biomed. Eng., vol. 57, pp. 2135-2146, 2010.  [13] E. Huupponen, G. Gomez-Herrero, A. Saastamoinen, A. Varri, J. Hasan, S.-L. Himanen, “Development and comparison of four sleep spindle detection methods,” Artif. Intell. Med., vol. 40, pp. 157-170, 2007. Support-Vector Machine (SVM) classifiers [15] N. Acır, and C. G)zelis, “Automatic recognition of sleep spindles in EEG by using artificial neural networks,” Expert Syst. Appl., vol. 27, no. 3, pp. 451–458, 2004. I. Mporas, P. Korvesis, E. Zacharaki, V. Megalooikonomou, “Sleep Spindle Detection in EEG Signals Combining HMMs and SVMs,” in Engineering Applications of Neural Networks, Communications in Computer and Information Science, vol. 384, L. Iliadis, H. Papadopoulos, S. Jayne Eds. Berlin: Springer, 2013, pp. 40-49. Matching Pursuit (MP) and wavelet techniques [16] J. Zygierewicz, K. J. Blinowska, P. J. Durka, W. Szelenberger, S. Niemcewicz, W. Androsiuk, “High resolution study of sleep spindles,” Clin. Neurophysiol., vol. 110, pp. 2136-2147, 1999. [17] S. V. Schonwald, E. L. de Santa-Helena, R. Rossatto, M. L. F. Chaves, G. J. L. Gerhardt, “Benchmarking matching pursuit to find sleep spindles,” J. Neurosci. Meth., vol. 156, no. 1–2, pp. 314–321, 2006. [18] F. Duman, A. Erdamar, O. Erogul, Z. Telatar, S. Yetkin, “Efficient sleep spindle detection algorithm with decision tree,” Expert Syst. Appl., vol. 36, pp. 9980-9985, 2009.  [21] L. Zhang, H. Li, and Y. Wei, “Sleep spindle detection using a novel instantaneous frequency definition,” Math. Meth. Appl. Sci., vol. 35, pp. 2101–2110, 2012. switching linear Gaussian state-space models [27] T. A. Camilleri, K. P. Camilleri, S. G. Fabri, “Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models,” Biomed. Signal Proces., vol. 10, pp. 117-127, 2014. Bayesian algorithms [28] B. I. Babadi, S. M. McKinney, V. Tarokh, J. M. Ellenbogen, "DiBa: a data-driven Bayesian algorithm for sleep spindle detection", IEEE Trans. Biomed. Eng., vol. 59, no. 2, pp. 483-493, 2012.

s****w

查文献，有很多方式：Artificial Neural Networks (ANN)、Support-Vector Machine (SVM) classifiers、Matching Pursuit (MP) and wavelet techniques、switching linear Gaussian state-space models、Bayesian algorithms、frequency and amplitude **ysis等，可是有谁用过吗？