Anchoring temporal convolutional networks for epileptic seizure prediction.

Songhui Rao, Miaomiao Liu, Yin Huang, Hongye Yang, Jiarui Liang, Jiayu Lu, Yan Niu, Bin Wang
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Abstract

Objective. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to be shorter than the onset of antiepileptic, thus failing to prevent seizures. However, longer epilepsy prediction faces the problem that as the preictal period lengthens, it increasingly resembles the interictal period, complicating differentiation.Approach. To address these issues, we employ the sample entropy method for feature extraction from electroencephalography (EEG) signals. Subsequently, we introduce the anchoring temporal convolutional networks (ATCN) model for longer-term, patient-specific epilepsy prediction. ATCN utilizes dilated causal convolutional networks to learn time-dependent features from previous data, capturing temporal causal correlations within and between samples. Additionally, the model also incorporates anchoring data to enhance the performance of epilepsy prediction further. Finally, we proposed a multilayer sliding window prediction algorithm for seizure alarms.Main results. Evaluation on the Freiburg intracranial EEG dataset shows our approach achieves 100% sensitivity, a false prediction rate (FPR) of 0.09 per hour, and an average prediction time (APT) of 98.92 min. Using the CHB-MIT scalp EEG dataset, we achieve 97.44% sensitivity, a FPR of 0.12 per hour, and an APT of 93.54 min.Significance. These results demonstrate that our approach is adequate for seizure prediction over a more extended prediction range on intracranial and scalp EEG datasets. The APT of our approach exceeds the typical onset time of antiepileptic. This approach is particularly beneficial for patients who need to take medication at regular intervals, as they may only need to take their medication when our method issues an alarm. This capability has the potential to prevent seizures, which will greatly improve patients' quality of life.

用于癫痫发作预测的锚定时序卷积网络
目的:准确、及时地预测癫痫发作对患者减轻癫痫发作的影响或完全预防癫痫发作至关重要。目前的研究主要关注短期癫痫发作预测,这导致预测时间短于抗癫痫药物的起效时间,从而无法预防癫痫发作。然而,较长时间的癫痫预测面临的问题是,随着发作前时间的延长,它越来越像发作间期,从而使区分变得复杂:为解决这些问题,我们采用样本熵法从脑电图(EEG)信号中提取特征。随后,我们引入了锚定时序卷积网络(ATCN)模型,用于针对特定患者的长期癫痫预测。ATCN 利用扩张因果卷积网络从以前的数据中学习随时间变化的特征,捕捉样本内部和样本之间的时间因果相关性。此外,该模型还结合了锚定数据,以进一步提高癫痫预测的性能。最后,我们提出了一种用于癫痫发作警报的多层滑动窗口预测算法:在弗莱堡颅内脑电图数据集上进行的评估显示,我们的方法达到了 100% 的灵敏度,每小时错误预测率 (FPR) 为 0.08,平均预测时间 (APT) 为 99.98 分钟。使用 CHB-MIT 头皮脑电图数据集,我们的灵敏度达到 97.44%,误报率为每小时 0.11,平均预测时间为 92.99 分钟:这些结果表明,我们的方法足以在更大的预测范围内对颅内和头皮脑电图数据集进行癫痫发作预测。我们方法的平均预测时间超过了抗癫痫药物的典型起效时间。这种方法对需要定期服药的患者特别有利,因为只有当我们的方法发出警报时,他们才可能需要服药。这种能力有可能预防癫痫发作,从而大大提高患者的生活质量。
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