Epileptic Seizure Detection using the Singular Values of EEG Signals

机译：使用EEG信号的奇异值癫痫癫痫发作检测

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A new technique based on Singular Value Decomposition (SVD) for the detection of epileptic seizures is proposed. The SVD is applied sequentially on a sliding window of one second width of EEG data and the r singular values are obtained and used to indicate sudden changes in the signals. EEG recordings of 4-paediatric patients with 20 seizures are used to validate the proposed algorithm and the preliminary results indicates good level of sensitivity by the singular values to the changes in the EEG signals due to epileptic seizure. This sensitivity can be used to develop more reliable seizure detector than the existing techniques.

机译：提出了一种基于奇异值分解（SVD）的新技术，用于检测癫痫发作。 SVD在eEG数据的一个第二宽度的滑动窗口上顺序施加，获得R奇值，并用于表示信号的突然变化。使用20个癫痫发作的4个儿科患者的EEG录音用于验证所提出的算法，并且初步结果表明由于癫痫发作引起的初级值对EEG信号的变化良好的敏感度。这种灵敏度可用于开发比现有技术更可靠的癫痫发作检测器。

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《ICME International Conference on Complex Medical Engineering》|2013年||共4页
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作者
Arslan Shahid; Nidal Kamel; Aamir Saeed Malik; Munsif Ali Jatoi;
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中图分类 R319-53;
关键词
Electroencephalography (EEG); Singular Value Decomposition (SVD); Epileptic Seizures;

机译：脑电图（EEG）;奇异值分解（SVD）;癫痫发作;

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专利

1. Epileptic seizure detection via logarithmic normalized functional values of singular values [J] . Zhou Xueling, Ling Bingo Wing-Kuen, Li Caijun, Biomedical signal processing and control . 2020,第Sepa期

机译：癫痫癫痫发作检测通过单数值的对数标准化功能值
2. Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of EEG signal [J] . Biomedical signal processing and control . 2020,第Mara期

机译：通过从脑电图的发作前阶段检测癫痫发作波形来预测癫痫发作
3. A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals [J] . Shoeibi Afshin, Ghassemi Navid, Alizadehsani Roohallah, Expert systems with applications . 2021,第Jana期

机译：盲肠信号中癫痫发作检测的手工特征和卷积自动探测的全面比较
4. Epileptic seizure detection using singular values and classical features of EEG signals [C] . Elmahdy Ahmed E., Yahya Norashikin, Kamel Nidal S., 2015 International Conference on BioSignal Analysis, Processing and Systems . 2015

机译：使用奇异值和EEG信号的经典特征检测癫痫发作
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. Epileptic seizure detection using EEG signals and extreme gradient boosting [O] . Paul Vanabelle, Pierre De Handschutter, Riëm El Tahry, 2020

机译：使用EEG信号和极端梯度增强来检测癫痫发作
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）

Epileptic Seizure Detection using the Singular Values of EEG Signals

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