Department of Informatics Engineering, CISUC, Univ Coimbra, Coimbra, Portugal.
Epilepsy Center, Department Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Sci Rep. 2022 Mar 15;12(1):4420. doi: 10.1038/s41598-022-08322-w.
Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm's decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 ([Formula: see text]38%) were solely validated by our methodology, while 24 ([Formula: see text]44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients.
癫痫发作预测可能是解决耐药性癫痫患者癫痫发作明显不可预测性的方法，这些患者约占所有癫痫患者的三分之一。设计癫痫发作预测模型涉及定义发作前期，即脑电活动从发作间期向发作放电过渡的阶段。这个阶段通常是一个固定的间隔，最近的一些研究报告了评估不同患者特异性发作前期间隔的情况。最近，研究人员旨在确定发作前期，即脑电活动从正常到癫痫发作的过渡阶段。由于这些模型具有自动进行预处理、特征提取、分类以及处理时间和空间依赖性的能力，作者一直在使用深度学习模型。由于这些方法创建了黑盒模型，临床医生可能没有足够的信心在高风险决策中使用它们。考虑到这些问题，我们开发了一种进化癫痫预测模型，该模型可以在自动搜索发作前期的同时识别最佳特征集，并考虑到患者的舒适度。这种方法提供了患者特异性的可解释性见解，这可能有助于更好地理解癫痫发作的产生过程，并解释算法的决策。我们在 93 名患有多种局灶性和全面性癫痫的患者的头皮记录中，对 238 次癫痫发作和 3687 小时的连续数据进行了测试。我们将结果与癫痫替代预测器进行了比较，并为 32%的患者获得了高于机会的性能。我们还将结果与基于标准机器学习管道（预处理、特征提取、分类器训练和后处理）的对照方法进行了比较，该对照方法通过验证 35%的患者，仅略优于我们的方法。总共有 54 名患者的至少一种方法（我们的方法或对照方法）表现优于机会水平。在这 54 名患者中，有 21 名（[Formula: see text]38%）仅通过我们的方法得到验证，而 24 名（[Formula: see text]44%）仅通过对照方法得到验证。这些发现可能表明需要针对不同的患者采用不同的方法。
