Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals

Abduljalil Mohamed; Khaled Bashir Shaban; Amr Mohamed

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Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals

机译：基于证据的加权分类器组合方法用于使用脑电信号的癫痫发作检测

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Different brain states and conditions can be captured by electroencephalogram (EEG) signals. EEG-based epileptic seizure detection techniques often reduce these signals into sets of discriminant features. In this work, an evidence theory-based approach for epileptic detection, using several classifiers, is proposed Within the framework of the evidence theory, each of these classifiers is considered a source of information and given a certain weight based on both its overall classification accuracy as well as its precision rate for the respective brain state. These sources are fused using the Dempster's rule of combination. Experimental work is done where five time domainfeatures are obtainedfrom EEG signals and used by a set classifiers, namely, Bayesian, K-nearest neighbor, neural network, linear discriminant analysis, and support vector machine classifiers. Higher classification accuracy of 89.5% is achieved, compared to 75.07% and 87.71% accuracy obtained from the worst and best used classifiers.

机译：脑电图（EEG）信号可以捕获不同的大脑状态和状况。基于EEG的癫痫发作检测技术通常会将这些信号降低为判别特征集。在这项工作中，提出了使用几种分类器的基于证据理论的癫痫检测方法。在证据理论的框架内，这些分类器中的每一个都被视为信息源，并基于其总体分类准确性而给予一定权重以及针对各个大脑状态的准确率。使用Dempster的组合规则将这些源融合在一起。从EEG信号获得五个时域特征并由一组分类器使用的实验工作已经完成，这些分类器是贝叶斯，K近邻，神经网络，线性判别分析和支持向量机分类器。与最差和最佳使用的分类器获得的75.07％和87.71％的准确性相比，可实现89.5％的更高分类精度。

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来源
《International journal of knowledge discovery in bioinformatics》 |2012年第2期|27-44|共18页
作者
Abduljalil Mohamed; Khaled Bashir Shaban; Amr Mohamed;
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作者单位

Computer Science and Engineering Department, College of Engineering, Qatar University, Qatar;

Computer Science and Engineering Department, College of Engineering, Qatar University, Qatar;

Computer Science and Engineering Department, College of Engineering, Qatar University, Qatar;

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原文格式 PDF
正文语种 eng
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关键词
Classifiers Combination; Decision Fusion; EEG Signal Classification; Epileptic Seizure Detection; Evidence Theory; Soft Computing;

机译：分类器组合;决策融合;脑电信号分类;癫痫发作检测;证据理论;软计算;

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1. Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers [J] . Mardini Wail, Yassein Muneer Masadeh Bani, Al-Rawashdeh Rana, Quality Control, Transactions . 2020,第期

机译：使用EEG信号与机器学习分类器结合使用EEG信号增强检测癫痫癫痫发作
2. 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
3. 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
4. A Study on EEG Signals for Epileptic Seizure Detection using Machine Learning Classifiers [C] . T. Jhansi Rani, D. Kavitha International Conference on Communication and Electronics Systems . 2021

机译：用机器学习分类机研究癫痫癫痫发作检测的脑电图
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. Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier [O] . Kh. Rezaee, E. Azizi, J. Haddadnia 2016

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
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）

Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals

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