August 31, 2020

EEG-Based Pathology Detection for Home Health Monitoring

Structured PICO

Does a deep convolutional network with feature fusion accurately detect pathology in EEG signals for home health monitoring?

Population

Publicly available EEG signal database containing normal and abnormal classes

Intervention

Deep convolutional network consisting of 1D and 2D convolutions with feature fusion and a multilayer perceptron (MLP) with two hidden layers

Comparator

Other network architectures including MLP with varying number of hidden layers and an autoencoder

Outcome

Accuracy of pathology detection

A deep convolutional network with feature fusion achieves >89% accuracy for EEG-based pathology detection, demonstrating potential for remote home health monitoring.

Abstract

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.

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