PDF(Pubmed)
Abstract：
BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease\'s burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios.
METHODS: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.
RESULTS: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.
CONCLUSIONS: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.
METHODS: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.
RESULTS: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.
CONCLUSIONS: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.
摘要：
背景：全球超过60%的癫痫患者是儿童，他们的早期诊断和治疗对他们的发展至关重要，可以大大减少疾病对家庭和社会的负担。已经提出了许多用于从EEG自动检测癫痫的算法。然而，在临床实践中，无法始终保证脑电图检查期间癫痫发作的发生。专门使用癫痫发作EEG进行检测风险的模型人为增强了性能指标。因此,迫切需要一种普遍适用的模型，可以在各种复杂的现实场景中执行自动癫痫检测。
方法：为了解决这个问题，我们设计了一种新技术，采用具有自我注意力的时间卷积神经网络（TCN-SA）。我们的模型包括两个主要成分：从EEG信号中提取时变特征的TCN，其次是一个自我注意（SA）层，赋予这些特征的重要性。通过关注关键特征，我们的模型提高了癫痫检测的分类准确性.
结果：在我们收集的小儿癫痫数据集和波恩数据集上验证了我们模型的有效性，在我们的数据集上达到95.50%的准确率，和97.37%(Av.E)，和93.50%(B对E)，分别。与其他深度学习架构(时间卷积神经网络，自我关注网络，和标准化的卷积神经网络)使用相同的数据集，我们的TCN-SA模型在癫痫自动检测方面表现优异.
结论：TCN-SA方法的有效性证明了其作为自动检测癫痫的有价值工具的潜力，在多样化和复杂的现实世界的临床设置提供显著的好处。
方法：为了解决这个问题，我们设计了一种新技术，采用具有自我注意力的时间卷积神经网络（TCN-SA）。我们的模型包括两个主要成分：从EEG信号中提取时变特征的TCN，其次是一个自我注意（SA）层，赋予这些特征的重要性。通过关注关键特征，我们的模型提高了癫痫检测的分类准确性.
结果：在我们收集的小儿癫痫数据集和波恩数据集上验证了我们模型的有效性，在我们的数据集上达到95.50%的准确率，和97.37%(Av.E)，和93.50%(B对E)，分别。与其他深度学习架构(时间卷积神经网络，自我关注网络，和标准化的卷积神经网络)使用相同的数据集，我们的TCN-SA模型在癫痫自动检测方面表现优异.
结论：TCN-SA方法的有效性证明了其作为自动检测癫痫的有价值工具的潜力，在多样化和复杂的现实世界的临床设置提供显著的好处。
