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Genetic Approach to Selecting the Optimal Feature for Epileptic Seizure Prediction.

机译：癫痫发作预测最佳特征选择的遗传算法。

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摘要

The objective of this study is to (1) develop and apply efficient algorithms to simultaneous intracranial electroencephalographic signals recorded from multiple implanted electrode sites to evaluate the spatial and temporal behavior of seizure precursors and (2) to demonstrate the utility of multiple feature and channel synergy for predicting epileptic seizures in patients with mesial temporal lobe epilepsy. Short-term seizure precursors within a 10-minute time period are investigated. The method consists of preprocessing, processing, feature selection, classification, and validation steps. The preprocessing step removes extraneous data and captures the salient signal attributes while maintaining the integrity of the signal. Processing is a three-step approach that includes first-level features extracted from the raw data, second-level features extracted from first level features, and third-level features extracted from second-level features. A genetic algorithm selects the optimal features off-line from a preselected group of features to serve as the input to the classifier.

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作者
D'Alessandro, M.; Vachtsevanos, G.; Hinson, A.; Esteller, R.; Echauz, J.;
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年度 2001
页码 1-5
总页数 5
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Electroencephalography; Epilepsy; Signal processing; Predictions; Classification; Preprocessing; Feature extraction; Seizures;

机译：脑电图;癫痫;信号处理;预测;分类;预处理;特征提取;癫痫发作;

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1. A multi-feature and multi-channel univariate selection process for seizure prediction. [J] . DAlessandro M, Vachtsevanos G, Esteller R, Clinical neurophysiology . 2005,第3期

机译：用于发作预测的多特征和多通道单变量选择过程。
2. Epileptic Seizure Detection Using Genetically Programmed Artificial Features [J] . Hiram Firpi, Erik D. Goodman, Javier Echauz IEEE Transactions on Biomedical Engineering . 2007,第期

机译：利用基因编程的人工特征进行癫痫发作的检测
3. Epileptic Seizure Detection Using Genetically Programmed Artificial Features [J] . Firpi H., Goodman E.D., Echauz J. IEEE Transactions on Biomedical Engineering . 2007,第2期

机译：利用基因编程的人工特征进行癫痫发作的检测
4. A genetic approach to selecting the optimal feature for epileptic seizure prediction [C] . DAlessandro, M., Vachtsevanos, Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE . 2001

机译：选择癫痫发作预测最佳特征的遗传方法
5. On prediction and detection of epileptic seizures by means of genetic programming artificial features. [D] . Firpi, Hiram Alexer. 2005

机译：关于通过遗传程序人工特征预测和检测癫痫发作的方法。
6. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach [O] . Lal Hussain 2018

机译：通过使用先进的参数优化方法使用强大的机器学习分类技术以不同的特征提取策略检测癫痫发作
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

Genetic Approach to Selecting the Optimal Feature for Epileptic Seizure Prediction.

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