Open AccessEEG signal classification method based on improved empirical mode decomposition and SVM
Author(s) -
Zihao Zhang,
Zhiyi Li,
Ting Ma,
Jiayu Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1846/1/012054
Subject(s) - hilbert–huang transform , electroencephalography , support vector machine , pattern recognition (psychology) , ictal , epilepsy , signal (programming language) , computer science , artificial intelligence , correlation coefficient , mode (computer interface) , mathematics , speech recognition , statistics , energy (signal processing) , neuroscience , psychology , machine learning , programming language , operating system
Epilepsy is a common phenomenon formed by abnormal discharges between brain neurons. The seizures of epilepsy are sudden and irregular. As a non-stationary signal, EEG signals can express its characteristics to a certain extent, and makes a significant difference in the monitoring and treatment of epilepsy diseases. This study employs empirical mode decomposition (EMD) to decompose the interictal and epileptic EEG signals into multiple eigenmode functions (IMF), and combines the correlation coefficient to screen the main IMF and extract its variance, fluctuation coefficient and Coefficient of variation and other features, combined with support vector machines for classification. Compared with the traditional empirical mode decomposition, this method has higher accuracy in the identification and classification of epileptic signals. The combination of this method not only provides a theoretical method for disease diagnosis and treatment, but also verifies the research and application value of EEG signals to a certain extent.