Päivinen Niina, Lammi Seppo, Pitkänen Asla, Nissinen Jari, Penttonen Markku, Grönfors Tapio
Department of Computer Science, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland.
Comput Methods Programs Biomed. 2005 Aug;79(2):151-9. doi: 10.1016/j.cmpb.2005.04.006.
This study concerns the detection of epileptic seizures from electroencephalogram (EEG) data using computational methods. Using short sliding time windows, a set of features is computed from the data. The feature set includes time domain, frequency domain and nonlinear features. Discriminant analysis is used to determine the best seizure-detecting features among them. The findings suggest that the best results can be achieved by using a combination of features from the linear and nonlinear realms alike.
本研究涉及使用计算方法从脑电图（EEG）数据中检测癫痫发作。通过短滑动时间窗口，从数据中计算出一组特征。该特征集包括时域、频域和非线性特征。使用判别分析来确定其中最佳的癫痫发作检测特征。研究结果表明，通过结合线性和非线性领域的特征可以取得最佳结果。
