While Deep Learning (DL) is often considered the state-of-the art for Artificial Intel-ligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have approached this issue in the context of online detection of epileptic seizures by developing a DL model from EEG signals, and associating certain properties of the model behavior with the expert medical knowledge. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: (1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; (2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and (3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures.

虽然深度学习 (DL) 通常被认为是基于人工智能的医疗决策支持的最新技术，但由于神经网络模型的可解释性不足，它在临床实践中仍然很少实施，并且临床医生的信任度很低。我们通过从 EEG 信号开发 DL 模型，并将模型行为的某些特性与专业医学知识相关联，在癫痫发作的在线检测的背景下解决了这个问题。这已经根据领域知识调整了输入信号的准备、网络架构和输出的后处理。具体来说，我们将讨论集中在三个主要方面：（1）如何将 DL 模型提供的信号段的分类结果聚合到更大的时间尺度，在癫痫级别；(2) 在不同模型的第一卷积层中学习到的相关频率模式是什么，以及它们与 EEG 视觉解释所依据的 delta、theta、alpha、beta 和 gamma 频段的关系；(3) 根据使用 DeepLIFT 方法突出显示的激活差异，识别对发作类贡献较大的信号波形。结果表明，第一层的内核大小决定了提取特征的可解释性和训练模型的敏感性，即使后处理后的最终性能非常相似。此外，我们发现振幅是导致发作预测的主要特征，这表明需要更多的患者群体来学习更复杂的频率模式。仍然，