Department of Neurosurgery, Oslo University Hospital, Norway.
Clin EEG Neurosci. 2012 Apr;43(2):97-104. doi: 10.1177/1550059411433611. Epub 2012 Mar 22.
We report comparison and assessment of the clinical utility of different automated methods for the estimation of the alpha frequency in electroencephalograph (EEG) and compare them with visual evaluation. A total of 56 consecutive patients, aged 17 to 78 years, who had a routine EEG recording, were included, and they were grouped as patients with epilepsy (Ep) and without epilepsy (nEp). Five different methods were used for alpha frequency estimation: visually guided manual counting and visually guided Fourier transform, and 3 methods were fully automated: time domain estimation of alpha (automatic assessment of alpha waves in time domain [ATD]) and 2 fast Fourier transform (FFT)-based methods, a segmented (automatic assessment of EEG segments by FFT) and one full FFT (automatic assessment of whole EEG by one FFT of the full recording [AWF]). The AWF discriminated significantly between Ep and nEp. Visually guided manual counting showed an almost significant difference independently in the 2 occipital electrodes. The ATD underestimated high frequencies and returned a too low mean frequency. This study shows that AWF is the best suited method for automatic assessment of the alpha frequency.
我们报告了不同自动方法在估计脑电图（EEG）中的α频率的临床实用性方面的比较和评估，并将其与视觉评估进行了比较。共有 56 名年龄在 17 至 78 岁之间的连续患者接受了常规脑电图记录，并将其分为癫痫（Ep）患者和无癫痫（nEp）患者。五种不同的方法用于估计α频率：视觉引导手动计数和视觉引导傅里叶变换，以及三种完全自动化的方法：时域估计α（时域中α波的自动评估 [ATD]）和两种基于快速傅里叶变换（FFT）的方法，一种分段（通过 FFT 对 EEG 段的自动评估）和一个完整 FFT（通过对整个记录的一个 FFT 进行整个 EEG 的自动评估 [AWF]）。AWF 可显著区分 Ep 和 nEp。视觉引导手动计数在两个枕部电极中独立显示出几乎显著的差异。ATD 低估了高频，返回的平均频率过低。本研究表明，AWF 是自动评估α频率的最佳方法。
