Predicting Seizures: Are We There Yet?

Sheryl Haut

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Predicting Seizures: Are We There Yet?

机译：预测癫痫发作：我们还在那里吗？

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Are we there yet? While the future of this particular device is uncertain due to funding limitations, the high sensitivity of prediction in this study is encouraging. Bold and innovative approaches—be they implanted devices, use of large collaborative international EEG databases (7, 8), or even smartphone based e-diaries (9)—steadily move us closer to solving the difficult problem of “unpredictability,” with the hope of reducing the burden of epilepsy.

机译：我们到了吗？尽管由于资金限制，该特定设备的未来不确定，但本研究中预测的高度敏感性令人鼓舞。大胆和创新的方法（无论是植入设备，使用大型协作性国际EEG数据库（7、8），还是基于智能手机的电子病历（9），都使我们逐步接近解决“不可预测性”这一难题。希望减轻癫痫负担。

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《Epilepsy currents》 |2013年第6期|共页
作者
Sheryl Haut;
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中图分类神经病学与精神病学;
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1. Prevalence of alexithymia in patients with psychogenic non-epileptic seizures and epileptic seizures and predictors in psychogenic non-epileptic seizures [J] . MyersL., MatznerB., LancmanM., Epilepsy & behavior: E&B . 2013,第2期

机译：心因性非癫痫性癫痫和癫痫发作患者的运动障碍症患病率及心因性非癫痫性癫痫发作的预测因子
2. Article Predicting Successful Generation and Inhibition of Seizure-like Afterdischarges and Mapping Their Seizure Networks Using fMRI [J] . Ben A. Duffy, ManKin Choy, Jin Hyung Lee Cell Reports . 2020,第8期

机译：文章预测成功产生和抑制癫痫发作的癫痫发作和使用FMRI映射其癫痫发作网络
3. Predictors of early recurrent seizure after first seizure presentation to an emergency service: A retrospective cohort study [J] . Seizure: the journal of the British Epilepsy Association . 2020,第期

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4. ANN based Predictive Model for identifying Epileptic Seizures with Random Forest Feature Selection [C] . Poorani. S, Harilatha. K, Balasubramanie. P, International Conference on Computing Methodologies and Communication . 2021

机译：随机森林特征选择鉴定癫痫发作的基于ANN的预测模型
5. Exploring Predictors of Treatment Efficacy in Patients With Psychogenic Non-Epileptic Seizures [D] . Dautoff, Rebecca J. 2019

机译：探索心理非癫痫发作患者治疗疗效的预测因子
6. Epileptiform Discharge and Electrographic Seizures during the Hypothermia Phase as Predictors of Rewarming Seizures in Children after Resuscitation [O] . Jainn-Jim Lin, Mei-Hsin Hsu, Shao-Hsuan Hsia, 2020

机译：在低温阶段期间癫痫型放电和拍摄癫痫发作作为复苏后儿童复苏癫痫发作的预测因子
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）

Predicting Seizures: Are We There Yet?

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