Deep Neural Architectures for Mapping Scalp to Intracranial EEG
Deep Neural Architectures for Mapping Scalp to Intracranial EEG
复制标题
DOI：
10.1142/s0129065718500090
复制
发表时间：
2018-10-01
影响因子：
8
通讯作者：
Took, Clive Cheong
中科院分区：
文献类型：
--
作者：
Antoniades, Andreas;Spyrou, Loukianos;Took, Clive Cheong
数据经常受到噪声的困扰，这些噪声阻碍了对临床有用的生物标志物的机器学习，脑电图（EEG）数据也不例外。颅内EEG（iEEG）数据增强了人类大脑深度学习模型的训练，但由于侵入性记录过程，通常是禁止的。一个更方便的替代方法是使用头皮电极记录大脑活动。然而，与头皮EEG数据相关联的固有噪声常常阻碍神经模型的学习过程，从而实现不合格的性能。在这里，提出了一种用于将头皮非线性映射到iEEG数据的集成深度学习架构。所提出的架构利用有限数量的联合头皮颅内记录的信息，以建立一种新的方法来检测癫痫放电的一般人群的受试者的sEEG。统计检验和定性分析表明，生成的伪颅内数据与真实颅内数据高度相关。这有助于从头皮记录中检测简易爆炸装置，因为这种波形通常不可见。作为现实世界的临床应用，这些伪iEEG然后被卷积神经网络用于在癫痫分析的背景下对颅内癫痫放电（IED）和非IED试验进行自动分类。虽然这项工作的目的是规避iEEG的不可用性和sEEG的局限性，我们已经实现了68%的分类准确率比以前提出的线性回归映射增加了6%。
Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.