Epileptic Seizure Detection using DWT Based Weighted Visibility Graph

Cai, Lihui; Wang, Jiang; Lu, Meili; Wei, Xile; Yu, Haitao; Wang, Ruofan; Fan, Yaqin

doi:10.1109/wcica.2018.8630498

Cited by 2 publications

(3 citation statements)

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“…The most common data representation-based method is to represent EEG signals in the time domain, the frequency domain, or their combination [5], [13]. However, the relationship between the sampling points of the EEG signal is ignored.…”

Section: A Eeg Signal Data Representation Methodsmentioning

confidence: 99%

“…The function f is the cross-entropy function [see (13)], and the second term is a square of L 2 regularization. Thus, the sum of both functions is differentiable.…”

Section: Admm Weight Pruning Methodsmentioning

confidence: 99%

“…However, most of the traditional methods require manual feature selection on EEG signals, which heavily relies on researcher's experience and domain knowledge of EEG signals. These methods can be biased when the human expert is subjective [13]. Therefore, it is of great value to propose an autonomous method for epileptic EEG signal classification.…”

Section: Introductionmentioning

confidence: 99%

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SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

Wang

Gao

Zheng

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.

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Section: A Eeg Signal Data Representation Methodsmentioning

confidence: 99%

“…The function f is the cross-entropy function [see (13)], and the second term is a square of L 2 regularization. Thus, the sum of both functions is differentiable.…”

Section: Admm Weight Pruning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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