Epilepsy as a dynamical disorder orchestrated by epileptogenic zone: a review

Yang, Chuanzuo; Liu, Zilu; Wang, Qingyun; Liu, Zhao; Luan, Guoming

doi:10.1007/s11071-021-06420-4

Cited by 18 publications

(9 citation statements)

References 141 publications

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“…Physiological experiments have shown that neuronal synchronization is closely related to many clinical diseases [ 5 , 6 ]. Therefore, it is necessary to discuss the synchronous transition of neurons.…”

Section: Discussionmentioning

confidence: 99%

“…Synchronous in the neuronal network, a complex population-firing pattern, is believed to play a critical role in many brain functions and many fundamental biological functions. For example, it has been implicated in sensory processing [ 1 ], the generation of sleep rhythms [ 2 ], the generation of respiratory rhythms [ 3 , 4 ], Parkinsonian tremor [ 5 ], epileptic seizures [ 6 – 8 ], and motor activity [ 9 ]. Synchronous arise through interactions between three network components.…”

Section: Introductionmentioning

confidence: 99%

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Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Yuan

2021

Computational Intelligence and Neuroscience

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Pre-Bötzinger complex (PBC) is a necessary condition for the generation of respiratory rhythm. Due to the existence of synaptic gaps, delay plays a key role in the synchronous operation of coupled neurons. In this study, the relationship between synchronization and correlation degree is established for the first time by using ISI bifurcation and correlation coefficient, and the relationship between synchronization and correlation degree is discussed under the conditions of no delay, symmetric delay, and asymmetric delay. The results show that the phase synchronization of two coupling PBCs is closely related to the weak correlation, that is, the weak phase synchronization may occur under the condition of incomplete synchronization. Moreover, the time delay and coupling strength are controlled in the modified PBC network model, which not only reveals the law of PBC firing transition but also reveals the complex synchronization behavior in the coupled chaotic neurons. Especially, when the two coupled neurons are nonidentical, the complete synchronization will disappear. These results fully reveal the dynamic behavior of the PBC neural system, which is helpful to explore the signal transmission and coding of PBC neurons and provide theoretical value for further understanding respiratory rhythm.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Yuan

2021

Computational Intelligence and Neuroscience

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“…Epilepsy is a kind of brain disorder resulting from abnormal firing of the neurons in the brain. 127 During the occurrence of seizures, fluctuation of the extracellular K + concentration occurs, which directly affects the membrane potential and has been one of the critical biomarkers for epilepsy diagnosis. 128,129 Mu ¨ller et al prepared a K + -sensitive fluorescent probe by encapsulating crown-ether-based K +specific fluoroionophore FI3 in cationic polymer RL100 nanoparticles.…”

Section: Brain Disordersmentioning

confidence: 99%

Inorganic ion-sensitive imaging probes for biomedical applications

Wang

Huang

et al. 2023

Chem. Commun.

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“…Epilepsy is a chronic neurological disease characterized by unusual behavior, sensations and loss of awareness [ 1 ]. According to the latest epidemiological data, 65 million people worldwide are affected by epilepsy [ 2 , 3 ], among which approximately 30% of patients cannot be controlled with anticonvulsants and surgery [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

Wang

Shi

et al. 2022

Entropy

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Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and am optimization algorithm for feature selection.

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Epilepsy as a dynamical disorder orchestrated by epileptogenic zone: a review

Cited by 18 publications

References 141 publications

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Inorganic ion-sensitive imaging probes for biomedical applications

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

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