Open Access

ARTICLE

Prediction of Epileptic EEG Signal Based on SECNN-LSTM

Jian Qiang Wang¹, Wei Fang^1,2,*, Victor S. Sheng³

1 School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
3 Department of Computer, Texas Tech University, Lubbock, TX 79409, USA

* Corresponding Author: Wei Fang. Email:

Journal of New Media 2022, 4(2), 73-84. https://doi.org/10.32604/jnm.2022.027040

Received 10 January 2022; Accepted 24 March 2022; Issue published 13 June 2022

Abstract

Brain-Computer Interface (BCI) technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life. People use this technology to capture brain waves and analyze the electroencephalograph (EEG) signal for feature extraction. Take the medical field as an example, epilepsy disease is threatening human health every moment. We propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases, overcoming the problem that the disease requires long time EEG monitoring and analysis by manual, which is a large workload and relatively subjective, and improving the prediction accuracy of epilepsy diseases by adding the attention mechanism module. Through experimental tests, the algorithm of SECNN-LSTM can effectively predict the EEG signal of epilepsy disease, and the correct recognition rate is improved. The experiment has some reference value for the subsequent research of EEG signals in other fields in deep learning.

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Keywords

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