Improving diagnostic accuracy of routine EEG for epilepsy using deep learning.

IF 4.5 Q1 CLINICAL NEUROLOGY
Brain communications Pub Date : 2025-08-25 eCollection Date: 2025-01-01 DOI:10.1093/braincomms/fcaf319
Émile Lemoine, Denahin Toffa, An Qi Xu, Jean-Daniel Tessier, Mezen Jemel, Frédéric Lesage, Dang Khoa Nguyen, Elie Bou Assi
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Abstract

The yield of routine EEG to diagnose epilepsy is limited by low sensitivity and the potential for misinterpretation of interictal epileptiform discharges. Our objective is to develop, train and validate a deep learning model that can identify epilepsy from routine EEG recordings, complementing traditional interpretation based on identifying interictal discharges. This is a retrospective cohort study of diagnostic accuracy. All consecutive patients undergoing routine EEG at our tertiary care centre between January 2018 and September 2019 were included. EEGs recorded between July 2019 and September 2019 constituted a temporally shifted testing cohort. The diagnosis of epilepsy was established by the treating neurologist at the end of the available follow-up period, based on clinical file review. Original EEG reports were reviewed for IEDs. We developed seven novel deep learning models based on Vision Transformers and Convolutional Neural Networks, training them to classify raw EEG recordings. We compared their performance to interictal discharge-based interpretation and two previously proposed machine learning methods. The study included 948 EEGs from 846 patients (820 EEGs/728 patients in training/validation, 128 EEGs/118 patients in testing). Median follow-up was 2.2 years and 1.7 years in each cohort, respectively. Our flagship Vision Transformer model, DeepEpilepsy, achieved an area under the receiver operating characteristic curve of 0.76 (95% confidence interval: 0.69-0.83), outperforming interictal discharge-based interpretation (0.69; 0.64-0.73) and previous methods. Combining DeepEpilepsy with interictal discharges increased the performance to 0.83 (0.77-0.89). DeepEpilepsy can identify epilepsy on routine EEG independently of interictal discharges, suggesting that deep learning can detect novel EEG patterns relevant to epilepsy diagnosis. Further research is needed to understand the exact nature of these patterns and evaluate the clinical impact of this increased diagnostic yield in specific settings.

应用深度学习提高癫痫常规脑电图诊断准确性。
常规脑电图诊断癫痫的灵敏度低,且有可能对间歇期癫痫样放电产生误解。我们的目标是开发、训练和验证一个深度学习模型,该模型可以从常规脑电图记录中识别癫痫,补充基于识别间歇放电的传统解释。这是一项诊断准确性的回顾性队列研究。纳入2018年1月至2019年9月期间在我们三级护理中心连续接受常规脑电图检查的所有患者。2019年7月至2019年9月期间记录的脑电图构成了暂时转移的测试队列。癫痫的诊断是由治疗神经科医生在可用的随访期结束时根据临床档案审查确定的。审查了简易爆炸装置的原始脑电图报告。我们开发了七个基于视觉变压器和卷积神经网络的新型深度学习模型,训练它们对原始脑电图记录进行分类。我们将它们的性能与基于间隔放电的解释和之前提出的两种机器学习方法进行了比较。该研究包括来自846例患者的948个脑电图(820个脑电图/728例训练/验证,128个脑电图/118例测试)。每个队列的中位随访时间分别为2.2年和1.7年。我们的旗舰Vision Transformer模型DeepEpilepsy实现了接受者工作特征曲线下的面积为0.76(95%置信区间:0.69-0.83),优于间隔放电解释(0.69;0.64-0.73)和之前的方法。深度癫痫合并间歇放电使评分提高到0.83(0.77-0.89)。深度癫痫患者可以独立于间歇放电在常规脑电图上识别癫痫,这表明深度学习可以检测出与癫痫诊断相关的新脑电图模式。需要进一步的研究来了解这些模式的确切性质,并评估这种增加的诊断率在特定情况下的临床影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
7.00
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审稿时长
6 weeks
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