SPaRCNet data: Seizures, Rhythmic and Periodic Patterns in ICU Electroencephalography

Seizures and seizure-like rhythmic and periodic brain activity known as “ictal-interictal-injury continuum” (IIIC) patterns are frequently detected during brain monitoring with electroencephalography (EEG) in patients with epilepsy or critical illness. Prior efforts to automate detection of IIIC patterns have been limited by lack of large well-annotated datasets to train/evaluate algorithms, and there have been only a few attempts to detect IIIC events other than seizures. The IIIC dataset includes  50,697 labeled EEG samples from 2,711 patients’ and 6,095 EEGs that were annotated by physician experts from 18 institutions. These samples were used to train SPaRCNet (Seizures, Periodic and Rhythmic Continuum patterns Deep Neural Network), a computer program that classifies IIIC events with an accuracy matching clinical experts. 

Associated GitHub repositories: 

https://github.com/bdsp-core/IIIC-IRR
https://github.com/bdsp-core/IIIC-SPaRCNet

癫痫或重症患者在接受脑电图（EEG）监测时，常可检测到被称为“发作-间歇期-损伤连续体”（IIIC）的癫痫样节律性和周期性脑活动模式。先前自动化检测IIIC模式的工作受到大型标注数据集匮乏的限制，以供算法训练与评估，且仅有少数尝试检测除癫痫外的IIIC事件。IIIC数据集包括由来自18个机构的医师专家标注的来自2,711名患者及6,095份脑电图的50,697个标注样本。这些样本被用于训练SPaRCNet（癫痫、周期性和节律性连续体模式深度神经网络），这是一个能够以与临床专家相当准确度对IIIC事件进行分类的计算机程序。