Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Unidad Guadalajara, Av. del Bosque 1145, col. El bajío, C.P. 45019, Zapopan, Jalisco, Mexico.
Comput Methods Programs Biomed. 2013 Jun;110(3):354-60. doi: 10.1016/j.cmpb.2013.01.014. Epub 2013 Mar 21.
Recent studies suggest that the appearance of signals with high frequency oscillations components in specific regions of the brain is related to the incidence of epilepsy. These oscillations are in general small in amplitude and short in duration, making them difficult to identify. The analysis of these oscillations are particularly important in epilepsy and their study could lead to the development of better medical treatments. Therefore, the development of algorithms for detection of these high frequency oscillations is of great importance. In this work, a new algorithm for automatic detection of high frequency oscillations is presented. This algorithm uses approximate entropy and artificial neural networks to extract features in order to detect and classify high frequency components in electrophysiological signals. In contrast to the existing algorithms, the one proposed here is fast and accurate, and can be implemented on-line, thus reducing the time employed to analyze the experimental electrophysiological signals.
最近的研究表明，大脑特定区域出现高频振荡信号与癫痫的发生有关。这些振荡通常幅度较小，持续时间较短，因此难以识别。对这些振荡的分析在癫痫中尤为重要，对其研究可能会导致更好的医疗治疗方法的发展。因此，开发用于检测这些高频振荡的算法非常重要。在这项工作中，提出了一种用于自动检测高频振荡的新算法。该算法使用近似熵和人工神经网络提取特征，以检测和分类电生理信号中的高频成分。与现有的算法相比，这里提出的算法快速准确，并且可以在线实现，从而减少了分析实验电生理信号所花费的时间。
