EEG Oscillatory Networks in Peri-Ictal Period of Absence Epilepsy

Li, Zhiye; Huang, Jialing; Wei, Wei; Jiang, Sili; Liu, Hong; Luo, Hua; Ruan, Jianghai

doi:10.3389/fneur.2022.825225

Cited by 3 publications

(6 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Differentiating between ictal and interictal EEG patterns in patients with typical absence seizures has long been a focal point for both clinicians and researchers. Recently, interest has grown in utilizing novel qEEG analysis techniques to further explore these phenomena (Antwi et al, 2019 ; Li et al, 2022 ; Springer et al, 2022 ; Zhong et al, 2022 ). The primary objective of our study was to investigate whether distinguishable differences exist in the spectral and entropy characteristics of EEGs in the pre-ictal and post-ictal periods compared to the interictal interval in a group of patients with typical absence seizures.…”

Section: Discussionmentioning

confidence: 99%

“…However, others have reported findings that lead to the opposite conclusion. For example, Li et al ( 2022 ), found a decrease in delta oscillations from the interictal to the pre-ictal state in a group of 21 patients with CAE. They proposed that this result could be explained by decreased cortical activity associated with slow oscillation in the brain.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns

Guerrero-Aranda,

Ramírez-Ponce,

Ramos-Quezada

et al. 2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns

Guerrero-Aranda,

Ramírez-Ponce,

Ramos-Quezada

et al. 2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Common abnormal rhythms observed in EEG mainly include spikes, sharp waves, spike‐slow complexes, sharp‐slow complexes, high rhythm disorders, paroxysmal rhythm waves, and positive spike rhythms, which can be used to identify seizures 4 . A further study found weaker alpha rhythm in the superior frontal gyrus among epilepsy patients compared with healthy people, and as the seizure goes, the delta and theta rhythms both turn to sighter, on contrary with alpha rhythm 39 . Another study reported that synchronization of brain neuronal activity enhanced during seizures, which was owe to decreasing autonomy of brain activity 40 .…”

Section: The Current State In the Field Of Seizure Detection Based On...mentioning

confidence: 91%

“…4 A further study found weaker alpha rhythm in the superior frontal gyrus among epilepsy patients compared with healthy people, and as the seizure goes, the delta and theta rhythms both turn to sighter, on contrary with alpha rhythm. 39 Another study reported that synchronization of brain neuronal activity enhanced during seizures, which was owe to decreasing autonomy of brain activity. 40 The variation of EEG rhythm apparently varies with each type of seizure.…”

Section: Discharge Of Brain Neurons In Seizuresmentioning

confidence: 99%

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

Wang

Lei

et al. 2022

Acta Neuro Scandinavica

View full text Add to dashboard Cite

With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband‐type devices, patch‐type devices, and armband‐type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure‐related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.

show abstract

“…In addition, drug naïve childhood absence epilepsy patients undergo significant alterations in resting-state networks of inter-ictal and ictal periods, resulting in dysfunction of specific networks essential for psychosocial functioning ( 15 ). However, by now, few studies ( 16 , 17 ) have been devoted to investigating the topological organization of functional brain networks in JAE patients with typical 3 Hz discharges and atypical discharges during resting state without the confounding effect of antiepileptic drugs and inter-ictal epileptic discharges.…”

Section: Introductionmentioning

confidence: 99%

Exploring brain network oscillations during seizures in drug-naïve patients with juvenile absence epilepsy

Tan,

Tang,

Luo

et al. 2024

Front. Neurol.

Self Cite

View full text Add to dashboard Cite

ObjectiveWe aimed to investigate the brain network activity during seizures in patients with untreated juvenile absence epilepsy.MethodsThirty-six juvenile absence epilepsy (JAE) patients with a current high frequency of seizures (more than five seizures during a 2 h EEG examination) were included. Each participant underwent a 2 h video EEG examination. Five 10 s EEG epochs for inter-ictal, pre-ictal, and post-ictal, and five 5 s EEG epochs for ictal states were extracted. Five 10 s resting-state EEG epochs for each participant from a sex- and age-matched healthy control (HC) were enrolled. The topological parameters of the brain networks were calculated using a graph theory analysis.ResultsCompared with the resting state of the HC group, the global efficiency, local efficiency, and clustering coefficients of the JAE group decreased in the inter-ictal state. In addition, the ictal state showed significantly increased global and local efficiency and clustering coefficients (p < 0.05) and a decreased small-world index and the shortest path length (p < 0.05) in the theta and alpha bands, compared to the remaining states within the JAE group. Moreover, subgroup analysis revealed that those JAE patients with typical 3 Hz discharges had upgraded global efficiency, local efficiency, and clustering coefficients in both delta and beta1 bands, compared to those JAE patients with non-3 Hz discharges during seizures.ConclusionThe present study supported the idea that the changes in the EEG brain networks in JAE patients are characterized by decreased global and local efficiency and clustering coefficient in the alpha band. Moreover, the onset of seizures is accompanied by excessively enhanced network efficiency. JAE patients with different ictal discharge patterns may have different functional network oscillations.

show abstract

EEG Oscillatory Networks in Peri-Ictal Period of Absence Epilepsy

Cited by 3 publications

References 54 publications

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

Exploring brain network oscillations during seizures in drug-naïve patients with juvenile absence epilepsy

Contact Info

Product

Resources

About