Epilepsy EEG Signal Classification Algorithm Based on Improved RBF

• Published in: Frontiers in neuroscience Vol. 14; p. 606
• Main Authors: Zhou, Dongmei, Li, Xuemei
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 23.06.2020; Frontiers Media S.A
• Subjects: Algorithms; Brain research; Classification; convolution neural network; EEG; EEG signal; Energy; Entropy; epilepsia; Epilepsy; Fuzzy sets; Nervous system; Neural networks; Neuroscience; one-against-one; RBF; Time series; Wavelet transforms
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2020.00606

Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.