Gifani P, Rabiee H R, Hashemi M R, Taslimi P, Ghanbari M
AmirKabir Univ. of Technol., Tehran.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:6225-8. doi: 10.1109/IEMBS.2006.260501.
The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during anesthesia has been proposed with the optimum Fractal Scaling Exponent. This optimum solution is based on the best box sizes in the Detrended Fluctuation Analysis (DFA) algorithm which have meaningful changes at different depth of anesthesia. The Fractal Scaling Exponent (FSE) Index as a new criterion has been proposed. The experimental results confirm that our new Index can clearly discriminate between aware to moderate and deep anesthesia levels. Moreover, it significantly reduces the computational complexity and results in a faster reaction to the transients in patients' consciousness levels in relations with the other algorithms.
近几十年来，麻醉深度估计一直是脑电信号处理领域最受关注的研究方向之一。在本文中，我们提出了一种通过量化脑电信号的动态波动来量化麻醉深度的新方法。利用最优分形标度指数，已提出提取麻醉期间大脑非线性动力学的有用信息。该最优解基于去趋势波动分析（DFA）算法中的最佳盒尺寸，这些尺寸在不同麻醉深度有显著变化。已提出分形标度指数（FSE）作为一个新的指标。实验结果证实，我们的新指标能够清晰地区分清醒、中度麻醉和深度麻醉水平。此外，与其他算法相比，它显著降低了计算复杂度，并能更快地响应患者意识水平的瞬态变化。
