Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed feature-based framework and deep neural networks including an EEG-optimized temporal convolutional network (TCN) to the task of pathological versus non-pathological EEG classification. For a robust comparison, we chose the Temple University Hospital (TUH) Abnormal EEG Corpus (v2.0.0), which contains approximately 3000 EEG recordings. The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks. We find accuracies across both approaches in an astonishingly narrow range from 81-86%. Moreover, visualizations and analyses indicated that both approaches used similar aspects of the data, e.g., delta and theta band power at temporal electrode locations. We argue that the accuracies of current binary EEG pathology decoders could saturate near 90% due to the imperfect inter-rater agreement of the clinical labels, and that such decoders are already clinically useful, such as in areas where clinical EEG experts are rare. We make the proposed feature-based framework available open source and thus offer a new tool for EEG machine learning research.

中文翻译：

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基于机器学习的脑电图病理诊断

机器学习 (ML) 方法具有使临床 EEG 分析自动化的潜力。它们可以分为基于特征（具有手工制作的特征）和端到端方法（具有学习特征）。以前关于 EEG 病理解码的研究通常分析了有限数量的特征、解码器或两者。对于 I) 更精细的基于特征的 EEG 分析，以及 II) 两种方法的深入比较，这里我们首先开发一个全面的基于特征的框架，然后将该框架与最先进的端到端框架进行比较- 结束方法。为此，我们将提出的基于特征的框架和深度神经网络（包括 EEG 优化的时间卷积网络 (TCN)）应用于病理与非病理 EEG 分类任务。为了进行稳健的比较，我们选择了天普大学医院 (TUH) 异常 EEG 语料库 (v2.0.0)，其中包含大约 3000 个 EEG 记录。结果表明，所提出的基于特征的解码框架可以达到与最先进的深度神经网络相同水平的精度。我们发现这两种方法的准确率都在 81-86% 的惊人范围内。此外，可视化和分析表明，这两种方法都使用了数据的相似方面，例如，时间电极位置处的 delta 和 theta 波段功率。我们认为，由于临床标签的评分者间一致性不完善，当前二进制 EEG 病理学解码器的准确度可能接近 90%，并且此类解码器已经在临床上有用，例如在临床 EEG 专家很少的领域。