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. © 2020 IEEE.

Vijay Kumar Chakka

Electrical Engineering

(SoE) School of Engineering

Mathur P.

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Shiv Nadar University

Graph Signal Processing of EEG signals for Detection of Epilepsy

2020 7th International Conference on Signal Processing and Integrated Networks, SPIN 2020

This paper proposes a method for gesture classification based on Graph Fourier transform (GFT) coefficients. GFT coefficients are the projection of image pixel block onto the eigenvectors of a Laplacian matrix. This Laplacian matrix is generated from undirected graph, representing a spatial connectedness between each pixel within an image block. This work proposes a method for generating an undirected graph by using edge information of the image. Edge information of the image is obtained by average sum of absolute difference between the current pixel and its neighboring pixels by using an appropriate threshold. The resulting GFT based feature vector is formed by concatenating GFT coefficients of each block. The resultant feature vector is applied to linear Support Vector Machine (SVM) classifier to predict the gesture class. For NTU and Massey hand gesture datasets, threshold value 30 gives maximum prediction accuracy. We compare the results of the proposed GFT based descriptor approach with Karhunen-Loeve transform (K-LT) and Discrete Cosine transform (DCT) based descriptors on three different gesture datasets: NTU, Cambridge and Massey. Simulation results show that the proposed GFT based descriptor gives a comparable results with Karhunen-Loeve transform (K-LT) and Discrete Cosine transform (DCT) based descriptors for gesture classification. © 2017 IEEE.

2017 4th International Conference on Image Information Processing, ICIIP 2017

Graph fourier transform based descriptor for gesture classification

IEEE Access

Detecting Seizures From EEG Signals Using the Entropy of Visibility Heights of Hierarchical Neighbors

2017 IEEE International Conference on Image Processing (ICIP)

Deep neural networks on graph signals for brain imaging analysis

Seizure

Epileptic seizure detection in EEGs signals based on the weighted visibility graph entropy

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 Lecture Notes in Computer Science

Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks

Graph signal processing of EEG signals for detection of epilepsy