Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Epilepsia. 2020 Sep;61(9):1906-1918. doi: 10.1111/epi.16628. Epub 2020 Aug 6.
Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes.
We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures.
From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures.
This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures.
在神经危重病学、癫痫诊断和管理以及闭环刺激或光遗传控制癫痫等新型疗法的实施中，癫痫发作检测是脑电图 (EEG) 分析的主要方面。它在高通量、稳健和可重复的临床前研究中也变得越来越重要。然而，由于验证有限，在临床或研究环境中，癫痫发作检测器并没有被广泛依赖。在这项研究中，我们创建了一种高性能的癫痫发作检测方法，在多个数据集上进行了验证，目的是为用户提供多种用途的系统。
我们引入了一种基于 141 个 EEG 信号特征的广义线性模型，用于对两个数据集的连续 EEG 中的癫痫发作进行分类。在第一个（局灶性癫痫）数据集，由 16 只局灶性癫痫大鼠组成，我们在 3 个月的 24/7 记录中收集了 1012 次自发性癫痫发作。我们在代表 20 个特征类别的 141 个特征上训练了广义线性模型，包括单变量和多变量、线性和非线性、时间和频率域。我们在多个保留测试数据集上测试了性能。然后，我们在第二个（多灶性癫痫）数据集，由 96 只大鼠和 2883 次自发性多灶性癫痫发作组成，使用经过训练的模型。
从局灶性癫痫数据集，我们构建了一个池化分类器，其接收者操作特征曲线下面积（AUROC）为 0.995，留一法分类器的 AUROC 为 0.962。我们在独立构建的多灶性癫痫数据集中验证了我们的方法，得到的池化 AUROC 为 0.963。我们分别验证了仅在局灶性癫痫数据集中训练并在保留的多灶性癫痫数据集中测试的模型，其 AUROC 为 0.890。超过 80%的癫痫发作的检测潜伏期不到 5 秒，超过 99%的癫痫发作的检测潜伏期不到 12 秒。
该方法在多个独立数据集上实现了最高的癫痫发作检测性能。这种癫痫发作检测方法可应用于自动脑电图分析管道以及闭环介入方法，在使用动物进行研究的情况下尤其有用，在这种情况下，需要标准化和高通量分析大量癫痫发作。
