Uher Daniel, Klimes Petr, Cimbalnik Jan, Roman Robert, Pail Martin, Brazdil Milan, Jurak Pavel
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:204-207. doi: 10.1109/EMBC44109.2020.9175734.
For a correct assessment of stereo-electroencephalographic (SEEG) recordings, a proper signal electrical reference is necessary. Such a reference might be physical or virtual. Physical reference can be noisy and a proper virtual reference calculation is often time-consuming. This paper uses the variance of the SEEG signals to calculate the reference from relatively low noise signals to reduce the contamination by distant sources, while maintaining negligible computing time.Ten patients with SEEG recordings were used in this study. 20-second long recordings from each patient, sampled at 5000 Hz, were used to calculate variances of SEEG signals and a low-variance (LV) subset of signals was selected for each patient. Consequently, 4 different reference signals were calculated using: 1) an average signal from WM contacts only (AVG_WM); 2) an average signal from LV contacts only (AVG_LV); 3) independent component analysis (ICA) method from WM contacts only (ICA_WM); and 4) ICA method from LV signals only (ICA_LV). Also, the original testing reference, an average signal from all SEEG contacts (AVG) was utilized. Finally, bipolar signals and average signals from anatomical structures were calculated and used to evaluate reference signals.91.7% of the WM SEEG contacts were found below the average variance. ICA_LV showed the best and AVG_WM the worst overall results. AVG_LV had the most positive impact on minimizing the mutual correlations between separate brain structures and correcting the outliers. The average processing time for ICA methods was 66.72 seconds and 0.7870 seconds for AVG methods (100 000 samples, 125.7±20.4 SEEG signals).Utilizing the LV data subset improves the reference signal. WM references are difficult to obtain and seem to be more susceptible to errors caused by low number of WM contacts in the dataset. ICA_LV can be considered as one of the best reference estimations, however the calculation is very demanding and time consuming. AVG_LV shows good and stable results, while it is based on a straightforward methodology and outstandingly fast calculation.
为了正确评估立体脑电图（SEEG）记录，需要一个合适的信号电参考。这样的参考可以是物理的或虚拟的。物理参考可能有噪声，而合适的虚拟参考计算通常很耗时。本文利用SEEG信号的方差，从相对低噪声的信号中计算参考，以减少远处源的污染，同时保持可忽略不计的计算时间。本研究使用了10例有SEEG记录的患者。从每位患者采集时长20秒、采样频率为5000Hz的记录，用于计算SEEG信号的方差，并为每位患者选择一个低方差（LV）信号子集。因此，使用以下方法计算了4种不同的参考信号：1）仅来自白质触点的平均信号（AVG_WM）；2）仅来自LV触点的平均信号（AVG_LV）；3）仅对白质触点使用独立成分分析（ICA）方法（ICA_WM）；4）仅对LV信号使用ICA方法（ICA_LV）。此外，还使用了原始测试参考，即来自所有SEEG触点的平均信号（AVG）。最后，计算并使用来自解剖结构的双极信号和平均信号来评估参考信号。发现91.7%的白质SEEG触点低于平均方差。ICA_LV显示出最佳总体结果，而AVG_WM最差。AVG_LV对最小化不同脑结构之间的相互相关性和校正异常值具有最积极的影响。ICA方法的平均处理时间为66.72秒，AVG方法为0.7870秒（100000个样本，125.7±20.4个SEEG信号）。利用LV数据子集可改善参考信号。白质参考难以获得，且似乎更容易受到数据集中白质触点数量少所导致误差的影响。ICA_LV可被视为最佳参考估计之一，然而其计算要求很高且耗时。AVG_LV显示出良好且稳定的结果，同时它基于一种直接的方法且计算速度极快。
