1 Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium.
2 IMEC VZW, 3001 Leuven, Belgium.
Int J Neural Syst. 2019 May;29(4):1850011. doi: 10.1142/S0129065718500119. Epub 2018 Apr 2.
Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.
确定核心特征集是开发自动癫痫检测器的最重要步骤之一。在大多数描述特征和癫痫分类器的已发表研究中，特征是手动设计的，这可能不是最优的。本文的主要目标是使用深度卷积神经网络（CNN）和随机森林自动优化特征选择和分类。所提出的分类器的输入是原始多通道 EEG，输出是类别标签：癫痫/非癫痫。通过对 26 名新生儿的 EEG 记录进行网络训练，对执行分类的五个末端层进行了替换，以便使用随机森林分类器来提高性能。使用包括可疑癫痫发作的 22 名新生儿的 EEG 记录测试集，该方法的假警报率为每小时 0.9 次，癫痫发作检测率为 77%。新提出的 CNN 分类器优于三种基于数据驱动的特征方法，并与先前开发的启发式方法性能相似。
