Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Parkville, Australia.
NeuroEngineering Lab, Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia.
Epilepsia. 2020 Feb;61(2):e7-e12. doi: 10.1111/epi.16418. Epub 2019 Dec 28.
Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.
癫痫发作预测是可行的，但需要更高的准确性，才能使癫痫发作预测在一大群患者中具有临床可行性。最近的一项工作在全球范围内的竞赛中众包了最先进的预测算法，提高了那些以前难以预测癫痫发作的患者的癫痫预测性能。本分析的目的是探索使用顶级竞赛算法的集合来提高性能的潜力。结果表明，性能可能会有微小的提高；然而，统计测试的结果限制了对这些提高的信心。我们的结果表明，对于特定的算法、评估框架和这里考虑的数据，递增式改进是可行的，但对于一些癫痫发作难以预测的患者，基于机器学习的癫痫预测性能可能存在上限。其他更具针对性的方法，例如，考虑到对发作前机制、患者特定的睡眠-觉醒节律或新的测量方法的更深入了解，可能仍然会为这些类型的患者带来进一步的收益。
