Durka Piotr J
Institute of Experimental Physics, Warsaw University, Ul. Hoza 69, 00-681 Warszawa, Poland.
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 May;69(5 Pt 1):051914. doi: 10.1103/PhysRevE.69.051914. Epub 2004 May 25.
Adaptive time-frequency approximations of signals have proven to be a valuable tool in electroencephalogram (EEG) analysis and research, where it is believed that oscillatory phenomena play a crucial role in the brain's information processing. This paper extends this paradigm to the nonoscillating structures such as the epileptic EEG spikes, and presents the advantages of their parametrization in general terms such as amplitude and half-width. A simple detector of epileptic spikes in the space of these parameters, tested on a limited data set, gives very promising results. It also provides a direct distinction between randomly occurring spikes or spike/wave complexes and rhythmic discharges.
信号的自适应时频逼近已被证明是脑电图(EEG)分析和研究中的一种重要工具,人们认为振荡现象在大脑信息处理中起着至关重要的作用。本文将这一范式扩展到非振荡结构,如癫痫性脑电图尖峰,并从幅度和半高宽等一般术语方面阐述了其参数化的优势。在这些参数空间中,一个简单的癫痫尖峰检测器在有限的数据集上进行了测试,结果非常有前景。它还能直接区分随机出现的尖峰或尖峰/波复合体与节律性放电。