EEG-Based Pathology Detection for 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.

Automated summary

A remote pathology detection system based on EEG with a deep convolutional network achieved over 89% accuracy on a publicly available EEG signal database. The system was also evaluated in a cloud-based framework and showed comparable performance to using a local server.