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Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

机译：优化的癫痫发作检测算法：使用GT判别分析和K-NN分类器快速诊断癫痫发作的方法

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Background Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder.

机译：背景癫痫病是中枢神经系统的一种严重疾病，使人容易发作反复发作。全世界有五千万人患有癫痫病；继阿尔茨海默氏症和中风之后，这是第三种广泛的神经疾病。

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期刊名称 Journal of Biomedical Physics Engineering
作者
Kh. Rezaee; E. Azizi; J. Haddadnia;
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作者单位

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年(卷),期 2016(6),2
年度 2016
页码 81–94
总页数 14
原文格式 PDF
正文语种
中图分类生物学;
关键词
EEG Signals Epileptic Seizure General Tensor Discriminant Analysis (GTDA) K-NN Wavelet Transform;

机译：脑电信号;癫痫发作;一般张量判别分析（GTDA）;K-NN;小波变换;
入库时间 2022-08-17 11:58:01

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专利

1. Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier [J] . E Azizi, J Haddadnia, Kh Rezaee Shiraz University of Medical Sciences . 2015,第2aJun期

机译：优化的癫痫发作检测算法：使用GT判别分析和K-NN分类器快速诊断癫痫发作的方法
2. Epileptic seizure detection in EEG signals using normalized IMFs in CEEMDAN domain and quadratic discriminant classifier [J] . Bari Md. Faizul, Fattah Shaikh Anowarul Biomedical signal processing and control . 2020,第Apra期

机译：使用CEEMDAN域中的归一化IMF和二次判别分类器对EEG信号进行癫痫发作检测
3. A novel fast epileptic seizure onset detection algorithm using general tensor discriminant analysis [J] . NasehiS., PourghassemH. Journal of Clinical Neurophysiology . 2013,第4期

机译：基于普通张量判别分析的新型快速癫痫发作发作检测算法
4. DWT based epileptic seizure detection from EEG signal using k-NN classifier [C] . Harender, R. K. Sharma 2017 International Conference on Trends in Electronics and Informatics . 2017

机译：使用k-NN分类器从脑电信号中基于DWT的癫痫发作检测
5. Detection of seizure onset in epileptic patients from intracranial EEG signals. [D] . Esteller, Rosana. 2000

机译：从颅内脑电图信号检测癫痫患者的癫痫发作。
6. Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier [O] . Md. Kamrul Hasan, Md. Asif Ahamed, Mohiuddin Ahmad, 2017

机译：通过使用k-NN分类器分析时间序列EEG信号预测癫痫发作
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）

Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

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