Oikonomou V P, Tzallas A T, Fotiadis D I
Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece.
Comput Methods Programs Biomed. 2007 Feb;85(2):101-8. doi: 10.1016/j.cmpb.2006.10.003. Epub 2006 Nov 16.
In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.
在这项工作中，我们提出了一种用于脑电图（EEG）记录中尖峰增强的方法。我们的方法利用时变自回归模型利用脑电图信号的非平稳特性。使用卡尔曼滤波器估计自回归模型的时变系数。结果表明，信噪比有显著提高，误报数量显著减少。
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