Ktonas P Y, Osorio P L, Everett R L
Electroencephalogr Clin Neurophysiol. 1979 Apr;46(4):382-8. doi: 10.1016/0013-4694(79)90139-1.
This paper describes a simple artifact detection algorithm which can be used when large amounts of EEG data are to be automatically processed via spectral analysis techniques in a general purpose digital computer, and visual inspection of each EEG epoch becomes an impossible task. The technique is based on a chi-square (chi(2)) goodness-of-fit test to a Gaussian distribution (CSQ), and it was applied to EEG epochs each 30 sec long. This test proved to be very sensitive to non-stationarities in the EEG amplitude distribution for a particular epoch, and it produced a large value for the chi(2) coefficient when an artifact was present. EEG epochs that gave rise to chi(2) coefficients of value larger than a heuristically determined minimum were discarded from further analysis. The above technique enabled efficient data reduction and reliable automatic off-line processing of 50 nights of sleep EEG via spectral techniques.
本文描述了一种简单的伪迹检测算法，当要通过通用数字计算机中的频谱分析技术自动处理大量脑电图（EEG）数据，且对每个EEG时段进行目视检查变得不可能时，该算法可派上用场。该技术基于对高斯分布进行卡方（chi(2)）拟合优度检验（CSQ），并应用于每个时长30秒的EEG时段。事实证明，该检验对特定时段的EEG幅度分布中的非平稳性非常敏感，当存在伪迹时，它会产生较大的卡方系数值。卡方系数值大于通过启发式确定的最小值的EEG时段将被舍弃，不再进行进一步分析。上述技术通过频谱技术实现了对50个夜间睡眠EEG的高效数据缩减和可靠的自动离线处理。
