Using genetic algorithms for automatic recurrent ANN development: an application to EEG signal classification

Daniel Rivero; Vanessa Aguiar-Pulido; Enrique Fernandez-Blanco; Marcos Gestal

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Using genetic algorithms for automatic recurrent ANN development: an application to EEG signal classification

机译：使用遗传算法进行自动循环神经网络自动开发：在脑电信号分类中的应用

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摘要

ANNs are one of the most successful learning systems. For this reason, many techniques have been published that allow the obtaining of feed-forward networks. However, few works describe techniques for developing recurrent networks. This work uses a genetic algorithm for automatic recurrent ANN development. This system has been applied to solve a well-known problem: classification of EEG signals from epileptic patients. Results show the high performance of this system, and its ability to develop simple networks, with a low number of neurons and connections.

机译：人工神经网络是最成功的学习系统之一。由于这个原因，已经公开了许多技术，这些技术允许获得前馈网络。但是，很少有著作描述用于开发循环网络的技术。这项工作使用遗传算法进行自动递归ANN开发。该系统已用于解决一个众所周知的问题：癫痫患者的EEG信号分类。结果显示了该系统的高性能以及开发简单网络的能力，其中神经元和连接的数量较少。

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《International journal of data mining, modelling and management》 |2013年第2期|182-191|共10页
作者
Daniel Rivero; Vanessa Aguiar-Pulido; Enrique Fernandez-Blanco; Marcos Gestal;
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作者单位

Fac. Informatica,University of A Coruna,Campus Elvina, 15071, A Coruna, Spain;

Fac. Informatica,University of A Coruna,Campus Elvina, 15071, A Coruna, Spain;

Fac. Informatica,University of A Coruna,Campus Elvina, 15071, A Coruna, Spain;

Fac. Informatica,University of A Coruna,Campus Elvina, 15071, A Coruna, Spain;

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正文语种 eng
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关键词
artificial neural networks; ANNs; genetic algorithms; GAs; signal classification; epilepsy detection;

机译：人工神经网络;人工神经网络遗传算法;加油站;信号分类癫痫检测;

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专利

1. Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm [J] . Ghaemi Alireza, Rashedi Esmat, Pourrahimi Ali Mohammad, Biomedical signal processing and control . 2017,第MARa期

机译：使用改进的二进制重力搜索算法在EEG信号中自动选择通道，以对大脑计算机界面中左手或右手运动进行分类
2. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals [J] . Michielli Nicola, Acharya U. Rajendra, Molinari Filippo Computers in Biology and Medicine . 2019,第期

机译：用于自动睡眠阶段分类的级联LSTM经常性神经网络，使用单通道EEG信号进行分类
3. Genetic Algorithm Optimization of Fuzzy Outputs for Classification of Epilepsy Risk Levels from EEG Signals [J] . R Harikumar, R Sukanesh, P A Bharathi Journal of the Institution of Engineers (India). Interdisciplinary Panels . 2005,第1期

机译：基于脑电信号的癫痫风险等级分类的模糊输出遗传算法优化
4. Automatic Recurrent ANN development for signal classification: detection of seizures in EEGs [C] . Daniel Rivero, Julian Dorado, Juan Rabunal, European Conference on Artificial Intelligence . 2008

机译：信号分类的自动复发性ANN开发：脑电图中癫痫发作的检测
5. A new sleep stage classification model using single-channel EEG signal analysis. [D] . Aboalayon, Khald Ali I. 2016

机译：使用单通道EEG信号分析的新型睡眠阶段分类模型。
6. Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals [O] . Rajesh Kumar Tripathy, Samit Kumar Ghosh, Pranjali Gajbhiye, 2020

机译：使用基于多变量投影的固定边界经验小波变换和从多机组EEG信号提取的自动睡眠阶段分类系统的开发
7. Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals [O] . Abhishek Varshney, Samit Kumar Ghosh, Sibasankar Padhy, 2021

机译：使用从多通道EEG信号获得的经常性神经网络和熵特征自动分类心理算术任务

Using genetic algorithms for automatic recurrent ANN development: an application to EEG signal classification

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