Abstract：
BACKGROUND: The seizure prediction algorithms have demonstrated their potential in mitigating epilepsy risks by detecting the pre-ictal state using ongoing electroencephalogram (EEG) signals. However, most of them require high-density EEG, which is burdensome to the patients for daily monitoring. Moreover, prevailing seizure models require extensive training with significant labeled data which is very time-consuming and demanding for the epileptologists.
METHODS: To address these challenges, here we propose an adaptive channel selection strategy and a semi-supervised deep learning model respectively to reduce the number of EEG channels and to limit the amount of labeled data required for accurate seizure prediction. Our channel selection module is centered on features from EEG power spectra parameterization that precisely characterize the epileptic activities to identify the seizure-associated channels for each patient. The semi-supervised model integrates generative adversarial networks and bidirectional long short-term memory networks to enhance seizure prediction.
RESULTS: Our approach is evaluated on the CHB-MIT and Siena epilepsy datasets. With utilizing only 4 channels, the method demonstrates outstanding performance with an AUC of 93.15% on the CHB-MIT dataset and an AUC of 88.98% on the Siena dataset. Experimental results also demonstrate that our selection approach reduces the model parameters and training time.
CONCLUSIONS: Adaptive channel selection coupled with semi-supervised learning can offer the possible bases for a light weight and computationally efficient seizure prediction system, making the daily monitoring practical to improve patients\' quality of life.
METHODS: To address these challenges, here we propose an adaptive channel selection strategy and a semi-supervised deep learning model respectively to reduce the number of EEG channels and to limit the amount of labeled data required for accurate seizure prediction. Our channel selection module is centered on features from EEG power spectra parameterization that precisely characterize the epileptic activities to identify the seizure-associated channels for each patient. The semi-supervised model integrates generative adversarial networks and bidirectional long short-term memory networks to enhance seizure prediction.
RESULTS: Our approach is evaluated on the CHB-MIT and Siena epilepsy datasets. With utilizing only 4 channels, the method demonstrates outstanding performance with an AUC of 93.15% on the CHB-MIT dataset and an AUC of 88.98% on the Siena dataset. Experimental results also demonstrate that our selection approach reduces the model parameters and training time.
CONCLUSIONS: Adaptive channel selection coupled with semi-supervised learning can offer the possible bases for a light weight and computationally efficient seizure prediction system, making the daily monitoring practical to improve patients\' quality of life.
摘要：
背景：通过使用持续的脑电图（EEG）信号检测发作前状态，癫痫发作预测算法已证明了其减轻癫痫风险的潜力。然而,大多数需要高密度脑电图，这对患者的日常监测来说是沉重的负担。此外，流行的癫痫发作模型需要使用大量标记数据进行大量训练，这对癫痫学家来说非常耗时和苛刻。
方法：为了应对这些挑战，在这里，我们分别提出了一种自适应通道选择策略和半监督深度学习模型，以减少脑电通道的数量，并限制准确预测癫痫发作所需的标记数据量。我们的通道选择模块集中在EEG功率谱参数化的特征上，这些特征可以精确表征癫痫活动，以识别每位患者的癫痫发作相关通道。半监督模型集成了生成对抗网络和双向长短期记忆网络以增强癫痫发作预测。
结果：我们的方法在CHB-MIT和锡耶纳癫痫数据集上进行了评估。只利用4个频道，该方法在CHB-MIT数据集上的AUC为93.15％，在Siena数据集上的AUC为88.98％。实验结果还表明，我们的选择方法减少了模型参数和训练时间。
结论：自适应通道选择与半监督学习相结合可以为轻量级和计算高效的癫痫发作预测系统提供可能的基础，使日常监测切实可行，以提高患者的生活质量。
方法：为了应对这些挑战，在这里，我们分别提出了一种自适应通道选择策略和半监督深度学习模型，以减少脑电通道的数量，并限制准确预测癫痫发作所需的标记数据量。我们的通道选择模块集中在EEG功率谱参数化的特征上，这些特征可以精确表征癫痫活动，以识别每位患者的癫痫发作相关通道。半监督模型集成了生成对抗网络和双向长短期记忆网络以增强癫痫发作预测。
结果：我们的方法在CHB-MIT和锡耶纳癫痫数据集上进行了评估。只利用4个频道，该方法在CHB-MIT数据集上的AUC为93.15％，在Siena数据集上的AUC为88.98％。实验结果还表明，我们的选择方法减少了模型参数和训练时间。
结论：自适应通道选择与半监督学习相结合可以为轻量级和计算高效的癫痫发作预测系统提供可能的基础，使日常监测切实可行，以提高患者的生活质量。
