Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals

Fan Miaolin; Chou Chun-An

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Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals

机译：通过脑电信号的时间同步检测癫痫发作的异常模式。

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Objective: Synchronization phenomena of epileptic electroencephalography (EEG) have long been studied. In this study, we aim at investigating the spatial-temporal synchronization pattern in epileptic human brains using the spectral graph theoretic features extracted from scalp EEG and developing an efficient multivariate approach for detecting seizure onsets in real time. Methods: A complex network model is used for representing the recurrence pattern of EEG signals, based on which the temporal synchronization patterns are quantified using the spectral graph theoretic features. Furthermore, a statistical control chart is applied to the extracted features overtime for monitoring the transits from normal to epileptic states in multivariate EEG systems. Results: Our method is tested on 23 patients from CHB-MIT Scalp EEG database. The results show that the graph theoretic feature yields a high sensitivity (similar to 98%) and low latency (similar to 6 s) on average, and seizure onsets in 18 patients are 100% detected. Conclusion: Our approach validates the increased temporal synchronization in epileptic EEG and achieves a comparable detection performance to previous studies. Significance: We characterize the temporal synchronization patterns of epileptic EEG using spectral network metrics. In addition, we found significant changes in temporal synchronization in epileptic EEG, which enable a patient-specific approach for real-time seizure detection for personalized diagnosis and treatment.

机译：目的：对癫痫性脑电图（EEG）的同步现象进行了长期的研究。在这项研究中，我们旨在使用从头皮脑电图提取的频谱图理论特征研究癫痫性人脑中的时空同步模式，并开发出一种有效的实时检测癫痫发作的多元方法。方法：使用复杂的网络模型表示脑电信号的重复模式，在此基础上，利用频谱图的理论特征对时间同步模式进行量化。此外，将统计控制图应用于超时提取的特征，以监视多元EEG系统中从正常状态到癫痫状态的转变。结果：我们的方法在来自CHB-MIT头皮脑电图数据库的23例患者中进行了测试。结果表明，图论理论特征平均产生高灵敏度（约98％）和低潜伏期（约6 s），并且100％检测到18例患者的癫痫发作。结论：我们的方法验证了癫痫性脑电图中时间同步性的提高，并实现了与先前研究相当的检测性能。启示：我们利用频谱网络指标描述了癫痫性脑电图的时间同步模式。此外，我们发现癫痫性脑电图在时间同步方面发生了重大变化，这使针对患者的实时癫痫发作检测方法可用于个性化诊断和治疗。

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来源
《IEEE Transactions on Biomedical Engineering》 |2019年第3期|601-608|共8页
作者
Fan Miaolin; Chou Chun-An;
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作者单位

Northeastern Univ, Dept Mech & Ind Engn, Boston, MA 02115 USA;

Northeastern Univ, Dept Mech & Ind Engn, Boston, MA 02115 USA;

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原文格式 PDF
正文语种 eng
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关键词
Complex network; data fusion; epileptic seizure; nonlinear dynamics; scalp epileptic electroencephalography (EEG); spectral graph theory;

机译：复杂网络;数据融合;癫痫发作;非线性动力学;头皮癫痫脑电图（EEG）;光谱图论;

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专利

1. Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns. [J] . Meier R, Dittrich H, Schulze-Bonhage A, Journal of Clinical Neurophysiology . 2008,第3期

机译：检测长期人类脑电图中的癫痫发作：自动在线和实时检测和多态性癫痫发作模式分类的新方法。
2. Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network [J] . Li Yang, Liu Yu, Cui Wei-Gang, IEEE transactions on neural systems and rehabilitation engineering . 2020,第4期

机译：使用统一的时间光谱挤压和激励网络在EEG信号中癫痫癫痫发作检测
3. CLASSIFICATION OF EEG SIGNALS FOR DETECTION OF EPILEPTIC SEIZURES BASED ON WAVELETS AND STATISTICAL PATTERN RECOGNITION [J] . Dragoljub Gajic, Zeljko Djurovic, Stefano Di Gennaro, Biomedical Engineering: Applications, Basis and Communications . 2014,第2期

机译：基于小波和统计模式识别的癫痫发作脑电信号分类
4. Spatio-Temporal Patterns in the Depth EEG During the Epileptic Seizure [C] . Jung Ae Kim, Sunyoung Cho, Sang Kun Lee, International Conference on Computational Science(ICCS 2005) pt.1; 20050522-25; Atlanta, GA(US) . 2005

机译：癫痫发作期间深度脑电图的时空格局
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. Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting [O] . Jiang Wu, Tengfei Zhou, Taiyong Li 2020

机译：通过互补集合经验模式分解和极端梯度升压检测EEG信号中的癫痫癫痫发作
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）

Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals

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