Baldassano Steven N, Brinkmann Benjamin H, Ung Hoameng, Blevins Tyler, Conrad Erin C, Leyde Kent, Cook Mark J, Khambhati Ankit N, Wagenaar Joost B, Worrell Gregory A, Litt Brian
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Brain. 2017 Jun 1;140(6):1680-1691. doi: 10.1093/brain/awx098.
There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care.
对于准确的自动癫痫发作检测算法，存在重大的临床和基础研究需求。这些算法在响应性神经刺激设备以及连续颅内脑电图数据的自动解析方面具有转化潜力。开发准确、经过验证的癫痫发作检测算法的一个重要障碍是难以获得来自长时间记录的高质量、经过专业注释的癫痫发作数据。为了克服这一问题，我们在kaggle.com上举办了一场竞赛，通过众包方式利用患有癫痫的犬类和人类的颅内脑电图来开发癫痫发作检测算法。然后，在样本外患者数据上对竞赛中表现最佳的三种算法进行了验证，这些数据包括标准临床数据以及使用植入式NeuroVista癫痫咨询系统在数年内获取的连续动态人体数据。来自世界各地的200个数据科学家团队参加了kaggle.com竞赛。表现最佳的团队提交了在样本外验证研究中性能一致且高度准确的算法。这些癫痫发作检测算法的性能是使用免费提供的代码和数据实现的，为个性化癫痫发作检测设定了一个新的可重复基准。我们还分享了一个“即插即用”的流程，以便其他研究人员能够轻松地在自己的数据集中使用这些算法。这场竞赛的成功表明，共享代码和高质量数据如何能够带来强大的转化工具，具有显著影响患者护理的潜力。
