Objective

In long-term video-monitoring, automatic seizure detection holds great promise as a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) designed to process images of EEG plots demonstrated high performance for seizure detection, but still have room for reducing the false-positive alarm rate.

Methods

We combined a CNN that processed images of EEG plots with patient-specific autoencoders (AE) of EEG signals to reduce the false alarms during seizure detection. The AE automatically logged abnormalities, i.e., both seizures and artifacts. Based on seizure logs compiled by expert epileptologists and errors made by AE, we constructed a CNN with 3 output classes: seizure, non-seizure-but-abnormal, and non-seizure. The accumulative measure of number of consecutive seizure labels was used to issue a seizure alarm.

Results

The second-by-second classification performance of AE-CNN was comparable to that of the original CNN. False-positive seizure labels in AE-CNN were more likely interleaved with “non-seizure-but-abnormal” labels than with true-positive seizure labels. Consequently, “non-seizure-but-abnormal” labels interrupted runs of false-positive seizure labels before triggering an alarm. The median false alarm rate with the AE-CNN was reduced to 0.034 h^-1, which was one-fifth of that of the original CNN (0.17 h^-1).

Conclusions

A label of “non-seizure-but-abnormal” offers practical benefits for seizure detection. The modification of a CNN with an AE is worth considering because AEs can automatically assign “non-seizure-but-abnormal” labels in an unsupervised manner with no additional demands on the time of the epileptologist.

中文翻译：

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基于头皮脑电图的卷积神经网络与自动编码器辅助的多级标记癫痫发作检测

目的

在长期的视频监控中，自动癫痫发作检测有望作为减轻癫痫专家工作量的一种手段。设计用于处理脑电图图像的卷积神经网络（CNN）表现出对癫痫发作检测的高性能，但仍有降低误报率的空间。

方法

我们将处理脑电图图像的CNN与脑电信号的患者特定自动编码器（AE）结合在一起，以减少癫痫发作检测期间的误报。AE自动记录异常，例如癫痫发作和伪影。基于专家癫痫专家汇编的癫痫发作日志和AE的错误，我们构建了CNN，具有3种输出类别：癫痫发作，非癫痫发作但异常和非癫痫发作。连续扣押标签数量的累积量度用于发出扣押警报。

结果

AE-CNN的第二到第二分类性能与原始CNN相当。AE-CNN中的假阳性癫痫发作标签比“真阳性癫痫发作标签”更容易与“非癫痫发作但异常”标签交错。因此，“非癫痫发作但异常”标签会在触发警报之前中断假阳性癫痫发作标签的运行。AE-CNN的误报率中值降低到0.034 h ^-1，是原始CNN（0.17 h ^-1）的五分之一。

结论

标签为“非癫痫发作但异常”可为癫痫发作检测提供实际益处。值得考虑的是用AE修改CNN，因为AE可以无监督的方式自动分配“非癫痫发作但异常”标签，而对癫痫医师的时间没有任何额外要求。