Jobert M, Scheuler W, Röske W, Poiseau E, Kubicki S
Abteilung für Klinische Neurophysiologie, Freie Universität Berlin.
EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb. 1991 Sep;22(3):178-86.
The evaluation of EEG-patterns is usually accomplished by visual analysis. Nowadays however, even personal computers are fast enough for an efficient pattern recognition of EEG signals. Using sleep spindles and K-complexes as examples, our aim was to demonstrate how patterns can be detected in an EEG signal with a high degree of accuracy. Furthermore, recognition of K-complexes has been improved by applying an additional "adaptive algorithm" allowing individual adjustments to the signal's form and amplitude.
脑电图模式的评估通常通过视觉分析来完成。然而如今，即使是个人电脑也足够快，能够对脑电图信号进行高效的模式识别。以睡眠纺锤波和K复合波为例，我们的目的是展示如何在脑电图信号中以高度的准确性检测模式。此外，通过应用一种额外的“自适应算法”，可以对信号的形式和幅度进行个体调整，从而提高了对K复合波的识别能力。
