Tzallas Alexandros T, Tsipouras Markos G, Fotiadis Dimitrios I
Unit of Medical Technology and Intelligent Information Systems, Department of Material Science and Technology, University of Ioannina, Ioannina 45110, Greece.
IEEE Trans Inf Technol Biomed. 2009 Sep;13(5):703-10. doi: 10.1109/TITB.2009.2017939. Epub 2009 Mar 16.
The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency (t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t-f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented.
在脑电图（EEG）片段中检测记录到的癫痫发作活动对于癫痫发作的定位和分类至关重要。然而，由于癫痫发作的演变通常是一个动态且非平稳的过程，并且信号由多个频率组成，基于视觉和传统频率的方法应用有限。在本文中，我们证明了时频（t-f）分析对于癫痫发作的脑电图片段分类的适用性，并比较了几种脑电图t-f分析方法。使用短时傅里叶变换和几种t-f分布来计算每个片段的功率谱密度（PSD）。分析分三个阶段进行：1）对每个脑电图片段进行t-f分析并计算PSD；2）特征提取，在特定的t-f窗口上测量信号片段的分数能量；3）使用人工神经网络对脑电图片段进行分类（是否存在癫痫发作）。使用从基准脑电图数据集中获得的三个分类问题对这些方法进行评估，并给出定性和定量结果。
