Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection have not been effectively harnessed due to sub-optimal classifier design and improper representation of the time-domain signal. Methods: In this study, we focused on designing and evaluating deep convolutional neural network-based classifiers for seizure detection. Signal-to-image conversion methods are proposed to convert time-domain EEG signal to a time-frequency represented image to prepare the input data for classification. We proposed and evaluated three classification methods comprising of five classifiers to determine which is more accurate for seizure detection. Accuracy data were then compared to previous studies of the same dataset. Results: We found our proposed model and signal-to-image conversion method outperformed all previous studies in the most cases. The proposed FT-VGG16 classifier achieved the highest average classification accuracy of 99.21%. In addition, the Shapley Additive exPlanations (SHAP) analysis approach was employed to uncover the feature frequencies in the EEG that contribute most to improved classification accuracy. To the best of our knowledge, this is the first study to compute the contribution of frequency components to target seizure classification; thus allowing the identification of distinct seizure-related EEG frequency components compared to normal EEG measures. Conclusion: Thus our developed deep convolutional neural network models are useful to detect seizures and characteristic frequencies using EEG data collected from the patients and this model could be clinically applicable for the automated seizures detection.

中文翻译：

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一种使用癫痫脑电图 (EEG) 数据检测癫痫发作和特征频率的深度卷积神经网络方法

背景：近年来，使用脑电图 (EEG) 结合深度学习计算方法诊断癫痫发作受到了广泛关注。然而，迄今为止，由于次优分类器设计和时域信号的不正确表示，癫痫检测中的深度学习技术尚未得到有效利用。方法：在这项研究中，我们专注于设计和评估基于深度卷积神经网络的分类器，用于癫痫检测。提出了信号到图像转换方法，将时域 EEG 信号转换为时频表示的图像，为分类准备输入数据。我们提出并评估了由五个分类器组成的三种分类方法，以确定哪种更准确地检测癫痫发作。然后将准确度数据与先前对同一数据集的研究进行比较。结果：我们发现我们提出的模型和信号到图像的转换方法在大多数情况下都优于之前的所有研究。提出的 FT-VGG16 分类器实现了最高的平均分类准确率 99.21%。此外，还采用了 Shapley Additive exPlanations (SHAP) 分析方法来揭示 EEG 中对提高分类准确性贡献最大的特征频率。据我们所知，这是第一项计算频率分量对目标癫痫分类的贡献的研究；因此，与正常 EEG 测量相比，可以识别与癫痫发作相关的不同 EEG 频率分量。结论：