EEG-Based Pathology Detection for Home Health Monitoring
EEG-Based Pathology Detection for Home Health Monitoring
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DOI：
10.1109/jsac.2020.3020654
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发表时间：
2021-02-01
影响因子：
16.4
通讯作者：
Kumar, Neeraj
中科院分区：
文献类型：
--
作者：
Muhammad, Ghulam;Hossain, M. Shamim;Kumar, Neeraj
本文提出了一种基于脑电的远程病理检测系统。该系统使用由1D和2D卷积组成的深度卷积网络。使用融合网络融合来自不同卷积层的特征。各种类型的网络进行了研究，类型包括一个多层感知器（MLP）与不同数量的隐藏层，和一个自动编码器。实验是使用一个公开的EEG信号数据库，其中包含两个类：正常和异常。实验结果表明，所提出的系统实现了大于89%的准确性，使用卷积网络，其次是MLP与两个隐藏层。所提出的系统也评估在一个基于云的框架，其性能被发现是使用本地服务器获得的性能相媲美。
An electroencephalogram (EEG)-based remote pathology detection system is proposed in this study. The system uses a deep convolutional network consisting of 1D and 2D convolutions. Features from different convolutional layers are fused using a fusion network. Various types of networks are investigated; the types include a multilayer perceptron (MLP) with a varying number of hidden layers, and an autoencoder. Experiments are done using a publicly available EEG signal database that contains two classes: normal and abnormal. The experimental results demonstrate that the proposed system achieves greater than 89% accuracy using the convolutional network followed by the MLP with two hidden layers. The proposed system is also evaluated in a cloud-based framework, and its performance is found to be comparable with the performance obtained using only a local server.