Epileptic Seizure Prediction in Scalp EEG using One Dimensional Local Binary Pattern based Features

机译：使用基于一维本地二进制图案的特征的头皮EEG中的癫痫癫痫发作预测

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Seizure prediction will deeply improve the quality of life of epileptic patients. In this paper, a new method of automatic seizure prediction is presented using one dimensional local binary pattern (1D-LBP) based features in scalp electroencephalogram (EEG). In the feature extraction stage, the preictal and interictal EEG signals were transformed to the 1D-LBP domain and histogram features were extracted. These features were submitted to two different types of classifiers: linear discriminant analysis (LDA) and support vector machine (SVM). In order to reduce the false prediction rate (FPR), a simple post processing stage was also incorporated. The classification using SVM showed improvement over LDA in terms of sensitivity, prediction time and FPR. The proposed method was evaluated using the scalp EEG recording from 13 patients with a total number of 47 seizures. It could achieve a sensitivity of 96.15%, an average prediction time of 51.25 minutes with an FPR of 0.463.

机译：癫痫发作预测将深深提高癫痫患者的生活质量。在本文中，使用基于头颅脑电图（EEG）中的一种基于局部二进制图案（1D-LBP）的特征来呈现一种新的自动癫痫发作预测方法。在特征提取阶段，将预见和嵌入的EEG信号转化为1D-LBP结构域，提取直方图特征。这些功能被提交给两种不同类型的分类器：线性判别分析（LDA）和支持向量机（SVM）。为了降低假预测率（FPR），还包含简单的后处理阶段。在灵敏度，预测时间和FPR方面，使用SVM的分类显示LDA对LDA的改进。使用来自13名患者的头皮EEG记录评估所提出的方法，总数为47次癫痫发作。它可以达到96.15％的敏感性，平均预测时间为51.25分钟，FPR为0.463。

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《International Conference on Bio-inspired Systems and Signal Processing》|2016年|1(CD-ROM)|共9页
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Thasneem Fathima; Paul Joseph K.; M. Bedeeuzzaman;
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中图分类 TB17-53;
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Epilepsy; Seizure Prediction; Electroencephalogram; Local Binary Pattern; Classifier;

机译：癫痫;癫痫发作预测;脑电图;局部二进制模式;分类器;

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1. Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction [J] . Samiee Kaveh, Kovacs Peter, Gabbouj Moncef Knowledge-Based Systems . 2017,第FEBa15期

机译：使用稀疏有理分解和局部Gabor二元模式特征提取对长期脑电记录中的癫痫发作进行检测
2. Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG [J] . P. P. Muhammed Shanir, Kashif Ahmad Khan, Yusuf Uzzaman Khan, Clinical EEG and neuroscience: official journal of the EEG and Clinical Neuroscience Society (ENCS) . 2018,第5期

机译：在长期脑电图中使用一维局部二元图案的形态特征自动癫痫发作检测
3. Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis [J] . Kaya Yilmaz Australasian physical & engineering sciences in medicine . 2015,第3期

机译：一维局部二进制模式的癫痫脑电图的隐藏模式发现和通过灰色关联分析检测癫痫发作
4. Epileptic Seizure Prediction in Scalp EEG using One Dimensional Local Binary Pattern based Features [C] . Thasneem Fathima, Paul Joseph K., M. Bedeeuzzaman International Conference on Bio-inspired Systems and Signal Processing . 2016

机译：使用基于一维本地二进制图案的特征的头皮EEG中的癫痫癫痫发作预测
5. Multi-Feature Analysis of Eeg Signal on Seizure Patterns and Deep Neural Structures for Prediction of Epileptic Seizures [D] . Ma, Xinyuan. 2020

机译：癫痫发作模式和深神经结构对癫痫癫痫发作预测的多种特征分析
6. Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals [O] . Turky N. Alotaiby, Saleh A. Alshebeili, Faisal M. Alotaibi, 2017

机译：使用CSP和LDA预测头皮脑电信号的癫痫发作预测
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）

Epileptic Seizure Prediction in Scalp EEG using One Dimensional Local Binary Pattern based Features

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