Graph Signal Processing of EEG signals for Detection of Epilepsy | IEEE Conference Publication | IEEE Xplore

Graph Signal Processing of EEG signals for Detection of Epilepsy


Abstract:

Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogram (EEG) signals. This paper proposes a Graph Signal Processing technique...Show More

Abstract:

Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogram (EEG) signals. This paper proposes a Graph Signal Processing technique called Graph Discrete Fourier Transform (GDFT) for the detection of epilepsy. EEG data points are projected on the Eigen space of Laplacian matrix of graph to produce GDFT coefficients. The Laplacian matrix is generated from weighted visibility graph constructed from EEG signals. It proposes Gaussian kernel based edge weights between the nodes. The proposed GDFT based feature vectors are then used to detect the seizure class from the given EEG signal using a crisp rule based classification. Simulation results show that the proposed GDFT based features from Gaussian Weighted Visibility Graph (VG) can detect epileptic seizure with 100 % accuracy.
Date of Conference: 27-28 February 2020
Date Added to IEEE Xplore: 20 April 2020
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Conference Location: Noida, India

I. Introduction

Graph Signal Processing (GSP) studies the signals having irregular domain residing on the nodes of a graph instead of regular intervals such as grids. To provide a nice compact format to encode the structure within the data, new tools are being developed in GSP [1]. Among various fields such as social network, gesture recognition, road network etc. Human Brain provides structure which can be easily analysed in graph signal domain. It provides the most inclusive information of psychological state of -a person. Epilepsy is one such kind of seizure that affects various psychological and physical functions. It is a neurological disorder where sudden abnormal reactions occur in the brain producing fluctuations which are captured in EEG signals.

References

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