Ahmedt-Aristizabal David, Fookes Clinton, Dionisio Sasha, Nguyen Kien, Cunha João Paulo S, Sridharan Sridha
The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.
Mater Centre for Neurosciences, Brisbane, Queensland, Australia.
Epilepsia. 2017 Nov;58(11):1817-1831. doi: 10.1111/epi.13907. Epub 2017 Oct 9.
Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.
癫痫是最常见的神经系统疾病之一，全球约有5000万人受其影响，近30%-40%的部分性癫痫患者对药物治疗无反应，因此癫痫手术被广泛认为是一种有效的治疗选择。基于视频监测、神经影像学、电生理和神经心理学测试的非侵入性技术使术前评估有了显著进展；然而，某些临床情况需要进行侵入性颅内记录，如立体定向脑电图（SEEG），旨在准确绘制癫痫发作期间涉及的明确脑网络。当前大多数术前评估程序侧重于半自动技术，手术诊断在很大程度上依赖于神经科医生的经验以及他们对癫痫症状学或癫痫表现及其与脑电活动相关性的耗时主观解读。由于手术误诊率达到30%，且超过三分之一的癫痫病例了解不足，因此人们对使用基于计算机的方法提高诊断精度有着明显的浓厚兴趣，在过去几年中这些方法已显示出近乎人类的性能。其中，深度学习在许多生物和医学应用中表现出色，但在癫痫评估和对症状学神经基础的自动理解方面进展不足。在本文中，我们系统地回顾了癫痫在人体运动分析、脑电活动以及解剖-电-临床相关性方面的自动应用，以将致痫网络的解剖定位归因于独特的癫痫模式。值得注意的是，将在癫痫背景下研究深度学习技术的最新进展，以应对传统机器学习技术所面临的挑战。最后，我们讨论并提出关于癫痫手术评估的未来研究方向，该研究可以跨视觉观察到的症状学模式和记录的脑电活动进行联合学习。
