EEG Recognition Method for Epileptic Patients Based on RNN Model with Attention Mechanism

被引：0
作者：
Zhou S. [1 ]
Gao T.-H. [1 ]
机构：
[1] School of Software, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2023年 / 44卷 / 08期
关键词：
attention mechanism; EEG (electroencephalogram); epileptic patient; RNN (recurrent neural networks) model; XGBoost classifier;
D O I：
10.12068/j.issn.1005-3026.2023.08.005
中图分类号：
学科分类号：
摘要：
A RNN (recurrent neural networks) model based on attention mechanism is proposed for EEG (electroencephalogram) data recognition in epilepsy patients. Traditional EEG feature analysis is time-consuming and excessively dependent on expert experience, which greatly limits the application and popularization of brain activity recognition methods. A new EEG recognition method to solve the above problems is proposed. Firstly, the basic characteristics of EEG from epilepsy patients are analyzed. Then, the RNN model based on attention mechanism is designed to eliminate various interference signals and the XGBoost classifier is used to identify the categories of EEG data, so as to achieve the purpose of automatic refinement and recognition of the original EEG. Finally, a large number of experiments are carried out on the public EEG data set to verify the accuracy of the proposed method. The experimental results show that compared with the mature EEG recognition methods, the proposed method has higher recognition accuracy. © 2023 Northeastern University. All rights reserved.
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页码：1098 / 1103
页数：5
相关论文
共 15 条
  • [1] Sukriti Chakraborty M, Mitra D., Epilepsy seizure detection using kurtosis based VMD's parameters selection and bandwidth features [ J], Biomedical Signal Processing and Control, 64, 9971, (2021)
  • [2] Wijayanto I, Hartanto R, Nugroho H A, Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electro-encephalo graphy signal[J], Informatics in Medicine Unlocked, 19, (2020)
  • [3] Lcdna B, Dt B, Raa B, Et al., Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-square support vector machine [ J], Informatics in Medicine Unlocked, 23, pp. 47-61, (2021)
  • [4] Zhao Yan-na, Method and system for identify epileptic EEG signals
  • [5] Wolpaw J R, Birbaumer N, Heetderks W J, Et al., Brain-computer interface technology: a review of the first international meeting [ J], Rehabilitation Engineering IEEE Transactions, 8, 2, pp. 164-173, (2000)
  • [6] Brari Z, Belghith S, A novel machine learning model for the detection of epilepsy and epileptic seizures using electroencephalographic signals based on chaos and fractal theories[ J], Mathematical Problems in Engineering, 2021, pp. 1-10, (2021)
  • [7] Schirrmeister R T, Springenberg J T, Fiederer L, Et al., Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG [ J ], Human Brain Mapping, 5, pp. 341-355, (2017)
  • [8] Correa A G, Orosco L L, Diez P, Et al., Adaptive filtering for epileptic event detection in the EEG[J], Journal of Medical and Biological Engineering, 1, pp. 82-93, (2019)
  • [9] Brari Z, Belghith S, A new method for the detection of epilepsy and epileptic seizures based on the variance of EEG signals and its derivatives with a simple kernel trick[ C], 2020 4th International Conference on Advanced Systems and Emergent Technologies (IC _ ASET), pp. 105-114, (2020)
  • [10] Yang J F., Applying a locally linear embedding algorithm for feature extraction and visualization of MI-EEG[J], Journal of Sensors, 16, 4, pp. 1-9, (2016)
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