Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires expertise and experience to identify abnormalities. It is thus crucial to develop automated models for the detection of abnormal EEGs related to epilepsy. This paper describes the development of a novel class of compact and efficient convolutional neural networks (CNNs) for detecting abnormal time intervals and electrodes in EEGs for epilepsy. The designed model is inspired by a CNN developed for brain-computer interfacing called multichannel EEGNet (mEEGNet). Unlike the EEGNet, the proposed model, mEEGNet, has the same number of electrode inputs and outputs to detect abnormalities. The mEEGNet was evaluated with a clinical dataset consisting of 29 cases of juvenile and childhood absence epilepsy labeled by a clinical expert. The labels were given to paroxysmal discharges visually observed in both ictal (seizure) and interictal (nonseizure) intervals. Results showed that the mEEGNet detected abnormal EEGs with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs. Moreover, the number of parameters is much smaller than other CNN models. To our knowledge, the dataset of absence epilepsy validated with machine learning through this research is the largest in the literature.
翻译:电脑神经系统(EEG)是诊断癫痫的关键,但需要专门知识和经验才能辨别异常,因此,开发自动模型以检测与癫痫有关的异常脑神经系统(EEEG)至关重要。本文描述了在癫痫发作的脑神经系统(EEEG)中,为检测异常时间间隔和电极而开发的新型紧凑而高效的神经神经系统(CNNs)新颖的一类紧凑而有效的神经系统(CNNs),用于检测癫痫发作的神经系统(EEEGNet),这与EEGNet的模型(MEEGNet)不同,即拟议的模型(MEEGNet)拥有相同数量的电极电极投入和产出来检测异常。MEGNet的临床数据集包括临床专家贴标签的29例青少年和儿童缺乏神经系统(CNN)神经系统(CIS)中检测到的脱氧气系统(MEGNet)和内部(NEGIS(非静脉冲)之间观察到的直观排放。结果显示,MEEGNet检测到的异常高电子电子数据与在曲线、F1-IS-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-V-S-S-S-S-S-V-V-V-S-S-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-V-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-