Does a deep convolutional network with feature fusion accurately detect pathology in EEG signals for home health monitoring?
A deep convolutional network with feature fusion achieves >89% accuracy for EEG-based pathology detection, demonstrating potential for remote home health monitoring.
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.
Muhammad et al. (2020) studied this question.