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Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy
Epilepsy & Behavior ( IF 2.6 ) Pub Date : 2019-10-01 , DOI: 10.1016/j.yebeh.2019.106556
Shannon Clarke , Philippa J. Karoly , Ewan Nurse , Udaya Seneviratne , Janelle Taylor , Rory Knight-Sadler , Robert Kerr , Braden Moore , Patrick Hennessy , Dulini Mendis , Claire Lim , Jake Miles , Mark Cook , Dean R. Freestone , Wendyl D'Souza

Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-electroencephalography (EEG) monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially heralding a more quantitative approach to therapeutic outcomes. There is substantial research into the automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic, and despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG. This study reports on a deep learning algorithm for computer-assisted EEG review. Deep convolutional neural networks were trained to detect epileptic discharges using a preexisting dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data were curated and confirmed independently by two epilepsy specialists (Seneviratne et al., 2016). The resulting automated detection algorithm was then used to review diagnostic scalp EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting. The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes. This article is part of the Special Issue "NEWroscience 2018".

中文翻译:

特发性全身性癫痫的计算机辅助脑电图诊断审查

癫痫诊断可能成本高昂、耗时且不准确。参考标准诊断监测是连续视频脑电图 (EEG) 监测,理想情况下捕获所有事件或一致的发作间期放电。自动化脑电图数据审查将节省时间和资源,从而使更多人能够接受参考标准监测,并可能预示着治疗结果的更量化方法。对从 EEG 自动检测癫痫发作和癫痫活动进行了大量研究。然而,自动检测软件在临床中并未得到广泛应用,尽管已发表了大量算法,但很少有方法获得监管机构批准从 EEG 检测癫痫活动。本研究报告了一种用于计算机辅助脑电图审查的深度学习算法。深度卷积神经网络经过训练,可以使用包含 103 名特发性全身性癫痫 (IGE) 患者队列中超过 6000 个标记事件的预先存在的数据集来检测癫痫放电。患者接受了 24 小时门诊脑电图检查,所有数据均由两名癫痫专家独立整理和确认(Seneviratne 等,2016)。然后使用由此产生的自动检测算法来检查七名患者(四名患有 IGE,三名患有模仿癫痫发作的事件)的诊断头皮脑电图,以验证临床环境中的表现。自动检测算法显示了从临床 EEG 检测癫痫活动的最先进性能,平均灵敏度 > 95%,相应的平均假阳性率为每分钟 1 次检测。重要的,诊断案例研究表明,自动检测算法将人工审核时间减少了 80%-99%,同时不会影响事件检测或诊断准确性。所呈现的结果表明,计算机辅助审查可以提高 EEG 评估的速度和准确性,并有可能大大改善治疗结果。本文是特刊“NEWroscience 2018”的一部分。
更新日期:2019-10-01
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