Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA.
The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic.
Sci Rep. 2019 Aug 6;9(1):11383. doi: 10.1038/s41598-019-47854-6.
The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification of EEG as showing normal physiological or abnormal pathological activity has been performed by expert visual review. The potential value of unbiased, automated EEG classification has long been recognized, and in recent years the application of machine learning methods has received significant attention. A variety of solutions using convolutional neural networks (CNN) for EEG classification have emerged with impressive results. However, interpretation of CNN results and their connection with underlying basic electrophysiology has been unclear. This paper proposes a CNN architecture, which enables interpretation of intracranial EEG (iEEG) transients driving classification of brain activity as normal, pathological or artifactual. The goal is accomplished using CNN with long short-term memory (LSTM). We show that the method allows the visualization of iEEG graphoelements with the highest contribution to the final classification result using a classification heatmap and thus enables review of the raw iEEG data and interpret the decision of the model by electrophysiology means.
脑电图 (EEG) 是神经生理学研究和临床神经病学的基石。从历史上看，通过专家视觉审查将 EEG 分类为显示正常生理或异常病理活动。无偏、自动 EEG 分类的潜在价值早已得到认可，近年来机器学习方法的应用受到了广泛关注。已经出现了多种使用卷积神经网络 (CNN) 进行 EEG 分类的解决方案，取得了令人印象深刻的结果。然而，CNN 结果的解释及其与潜在基本电生理学的联系尚不清楚。本文提出了一种 CNN 架构，该架构能够解释颅内 EEG (iEEG) 瞬态，从而驱动大脑活动的正常、病理或人为分类。该目标是通过具有长短期记忆 (LSTM) 的 CNN 来实现。我们表明，该方法允许使用分类热图可视化对最终分类结果贡献最大的 iEEG 图形元素，从而可以查看原始 iEEG 数据并通过电生理学手段解释模型的决策。
