Department of Clinical Neurophysiology, University Hospital of Linköping, Sweden; Center for Social and Affective Neuroscience, Linköping University, Sweden; Center for Medical Image Science and Visualization, Linköping University, Sweden; Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
Department of Clinical Neurophysiology, University Hospital of Linköping, Sweden; Center for Social and Affective Neuroscience, Linköping University, Sweden; Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
J Neurosci Methods. 2021 May 1;355:109126. doi: 10.1016/j.jneumeth.2021.109126. Epub 2021 Mar 9.
In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results.
Generative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21-channel EEGs. 5,144 h of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks.
The results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5-100 subjects.
Using neural networks to restore or upsample EEG signals is a viable alternative to spherical spline interpolation.
在临床实践中，脑电图是通过视觉评估的。出于实际原因，记录通常需要使用较少的电极进行，并且伪影使得评估变得困难。为了规避这些障碍，可以使用不同的插值技术。这些技术通常在电极密度较高的情况下表现更好，并且在远离电极的区域插值的值可能不可靠。使用一种学习皮质电场统计分布并预测值的方法可能会产生更好的结果。
基于卷积层的生成网络被训练为从 4 个或 14 个通道上进行上采样，或者动态恢复单个缺失的通道，以重新创建 21 通道的脑电图。使用来自 Temple 大学医院脑电图数据库的 1385 名受试者的 5144 小时数据来训练和评估网络。
将结果与球形样条插值进行比较。使用了几种统计措施以及由经过认证的临床神经生理学家进行的视觉评估。总体而言，生成网络的性能明显更好。在经验丰富的脑电图解释器评估为人工的示例数量方面，真实数据和网络生成数据之间没有差异，而对于通过插值生成的数据，数量则明显更高。此外，网络性能随着包含的受试者数量的增加而提高，在 5-100 名受试者的范围内效果最大。
使用神经网络来恢复或上采样脑电图信号是球形样条插值的一种可行替代方法。
