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Detection of epilepsy seizures and epileptic indicators in EEG signals [EEG i̇şareti üzerinde sara ataklarinin ve sara belirtisi i̇şaretlerin bulunmasi]

机译：检测脑电信号中的癫痫发作和癫痫指标[发现癫痫发作和脑电标志上的癫痫迹象]

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Symptoms of epilepsy, which is characterized by abnormal brain electrical activity, can be observed on electroencephalography (EEG) signal. This paper employs models of chaotic measures of EEG and aims to help detection of epilepsy seizures and diagnosis of epileptic indicators in seizure-free signals. ©2008 IEEE.

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Yücel, Z.; Özgüler, A.B.;
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年度 2008
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正文语种 Turkish
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专利

1. 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
2. A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques [J] . Biomedical signal processing and control . 2020,第Feba期

机译：基于小波分析和算术编码的新颖方法，用于使用机器学习技术自动检测和诊断脑电信号中的癫痫发作
3. Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals [J] . Miran Lee, Jaehwan Ryu, Deok‐Hwan Kim ETRI journal . 2020,第2期

机译：自动癫痫癫痫发作波形检测方法，基于使用隐马尔可夫模型和脑电图信号的小波系数计数的平均斜率的特征
4. A new method for the detection of epilepsy and epileptic seizures based on the variance of EEG signals and its derivatives with a simple kernel trick [C] . Zayneb Brari, Safya Belghith International Conference on Advanced Systems and Emergent Technologies . 2020

机译：一种新方法，用于检测癫痫和癫痫癫痫发作的脑电图和衍生物与简单的内核伎俩
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. Long-Term V-EEG in Epilepsy: Chronological Distribution of Recorded Events Focused on the Differential Diagnosis of Epileptic Seizures and Psychogenic Non-Epileptic Seizures [O] . Fernando Vázquez-Sánchez, Beatriz García-López, Ana Isabel Gómez-Menéndez, 2021

机译：癫痫中的长期V-eeg：记录事件的时间顺序分布重点是癫痫发作和心理非癫痫发作的差异诊断
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）

Detection of epilepsy seizures and epileptic indicators in EEG signals [EEG i̇şareti üzerinde sara ataklarinin ve sara belirtisi i̇şaretlerin bulunmasi]

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