WTRPNet: An Explainable Graph Feature Convolutional Neural Network for Epileptic EEG Classification
Author:
Affiliation:
1. Beijing Jiaotong University, Haidian Qu, Beijing Shi, China
2. Heibei University, Baoding Shi, Hebei Province, China
3. University of Leicester, Leicester, UK
Abstract
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hebei Province
Science Research Project of Hebei Province
Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology
High-Performance Computing Center of Hebei University
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture
Link
https://dl.acm.org/doi/pdf/10.1145/3460522
Reference42 articles.
1. APPLICATION OF RECURRENCE QUANTIFICATION ANALYSIS FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS
2. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients;Andrzejak Ralph G.;Physical Review E,2012
3. Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals;Bhattacharyya Abhijit;Applied Sciences,2017
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