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Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation.
JAMA Neurology ( IF 20.4 ) Pub Date : 2019-10-21 , DOI: 10.1001/jamaneurol.2019.3485 Jin Jing 1, 2 , Haoqi Sun 1 , Jennifer A Kim 1 , Aline Herlopian 3 , Ioannis Karakis 4 , Marcus Ng 5 , Jonathan J Halford 6 , Douglas Maus 1 , Fonda Chan 1 , Marjan Dolatshahi 1 , Carlos Muniz 1 , Catherine Chu 1 , Valeria Sacca 7 , Jay Pathmanathan 8 , Wendong Ge 1 , Justin Dauwels 2 , Alice Lam 1 , Andrew J Cole 1 , Sydney S Cash 1 , M Brandon Westover 1
JAMA Neurology ( IF 20.4 ) Pub Date : 2019-10-21 , DOI: 10.1001/jamaneurol.2019.3485 Jin Jing 1, 2 , Haoqi Sun 1 , Jennifer A Kim 1 , Aline Herlopian 3 , Ioannis Karakis 4 , Marcus Ng 5 , Jonathan J Halford 6 , Douglas Maus 1 , Fonda Chan 1 , Marjan Dolatshahi 1 , Carlos Muniz 1 , Catherine Chu 1 , Valeria Sacca 7 , Jay Pathmanathan 8 , Wendong Ge 1 , Justin Dauwels 2 , Alice Lam 1 , Andrew J Cole 1 , Sydney S Cash 1 , M Brandon Westover 1
Affiliation
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Importance
Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability.
Objective
To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs.
Design, Setting, and Participants
A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built.
Main Outcomes and Measures
SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation.
Results
SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865).
Conclusions and Relevance
In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.
中文翻译：
脑电图解释期间专家级自动检测癫痫样放电的发展。
重要性脑电图（EEG）中的发作期癫痫样放电（IED）是癫痫，癫痫发作风险和临床下降的生物标志。但是，缺乏合格的专家来解释脑电图结果。先前自动进行IED检测的尝试受到小样本的限制，并且没有表现出专家级的性能。需要一种经过验证的自动化方法来检测具有专家级可靠性的IED。目的开发并验证一种计算机算法，该算法能够像专家一样可靠地识别IED，并将EEG记录分类为包含IED而不包含IED。设计，设置和参与者总共9571个有和没有IED的头皮脑电图记录用于训练深层神经网络（SpikeNet）进行IED检测。由13 262名IED候选人生成了独立的培训和测试数据集，并由8名受过研究金培训的临床神经生理学家分别进行了注释，并根据临床EEG报告生成了8520条不含IED的EEG记录。使用估计的尖峰概率，还建立了将整个EEG记录指定为正或负的分类器。主要结果和措施SpikeNet的准确性，敏感性和特异性与受过奖学金研究的神经生理学专家进行比较，以鉴定IED并将EEG分类为IED的阳性或阴性或阴性。统计性能通过校准误差和接收器工作特性曲线（AUC）下的面积进行评估。所有性能统计数据均使用10倍交叉验证进行估算。结果基于校准误差，SpikeNet超越了专家的解释和行业标准的商用IED检测器（SpikeNet，0.041; 95％CI，0.033-0.049; vs行业标准，0.066; 95％CI，0.060-0.078; vs专家，均值， 0.183;范围0.081-0.364）和基于AUC的二进制分类性能（SpikeNet，0.980; 95％CI，0.977-0.984; vs行业标准，0.882; 95％CI，0.872-0.893）。相对于专家（平均值，0.197;范围，0.099-0.372），整个EEG分类的平均校准误差为0.126（范围，0.109-0.1444），AUC为0.847（95％CI，0.830-0.865）。结论与相关性在这项研究中，SpikeNet自动检测了IED，并将整个EEG归为IED阳性或IED阴性。
更新日期：2020-01-13
中文翻译：
脑电图解释期间专家级自动检测癫痫样放电的发展。
重要性脑电图（EEG）中的发作期癫痫样放电（IED）是癫痫，癫痫发作风险和临床下降的生物标志。但是，缺乏合格的专家来解释脑电图结果。先前自动进行IED检测的尝试受到小样本的限制，并且没有表现出专家级的性能。需要一种经过验证的自动化方法来检测具有专家级可靠性的IED。目的开发并验证一种计算机算法，该算法能够像专家一样可靠地识别IED，并将EEG记录分类为包含IED而不包含IED。设计，设置和参与者总共9571个有和没有IED的头皮脑电图记录用于训练深层神经网络（SpikeNet）进行IED检测。由13 262名IED候选人生成了独立的培训和测试数据集，并由8名受过研究金培训的临床神经生理学家分别进行了注释，并根据临床EEG报告生成了8520条不含IED的EEG记录。使用估计的尖峰概率，还建立了将整个EEG记录指定为正或负的分类器。主要结果和措施SpikeNet的准确性，敏感性和特异性与受过奖学金研究的神经生理学专家进行比较，以鉴定IED并将EEG分类为IED的阳性或阴性或阴性。统计性能通过校准误差和接收器工作特性曲线（AUC）下的面积进行评估。所有性能统计数据均使用10倍交叉验证进行估算。结果基于校准误差，SpikeNet超越了专家的解释和行业标准的商用IED检测器（SpikeNet，0.041; 95％CI，0.033-0.049; vs行业标准，0.066; 95％CI，0.060-0.078; vs专家，均值， 0.183;范围0.081-0.364）和基于AUC的二进制分类性能（SpikeNet，0.980; 95％CI，0.977-0.984; vs行业标准，0.882; 95％CI，0.872-0.893）。相对于专家（平均值，0.197;范围，0.099-0.372），整个EEG分类的平均校准误差为0.126（范围，0.109-0.1444），AUC为0.847（95％CI，0.830-0.865）。结论与相关性在这项研究中，SpikeNet自动检测了IED，并将整个EEG归为IED阳性或IED阴性。
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