Correlation of EEG with neuropsychological status in children with epilepsy

Hsu, David; Rayer, Katherine; Jackson, Daren C.; Stafstrom, Carl E.; M, Hsu; Ferrazzano, Peter; Dabbs, Kevin; Worrell, Gregory A.; Jones, Jana E.; Hermann, Bruce P.

doi:10.1016/j.clinph.2015.07.027

Cited by 7 publications

(4 citation statements)

References 55 publications

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“…It is important to note that all of this work has been done on high school or college students in high income countries and used school grade scores for prediction. A few studies in North America and China have looked at correlations between EEG and neuropsychological or academic status of younger children indicating EEG spectrum to be sensitive to attention-deficit hyperactive disorder [39,40] and predictive of emergent math skills in preschoolers in another study [41]. To the best of our knowledge, our study is the first in a developing country to use EEG data collected on children at the age of 4 years to use ML to predict early school failure in early primary grades.…”

Section: Plos Onementioning

confidence: 99%

Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan

Rasheed

Chand

Ahmed³

et al. 2021

PLoS ONE

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Universal primary education is critical for individual academic growth and overall adult productivity of nations. Estimates indicate that 25% of 59 million primary age out of school children drop out and early grade failure is one of the factors. An objective and feasible screening measure to identify at-risk children in the early grades can help to design appropriate interventions. The objective of this study was to use a Machine Learning algorithm to evaluate the power of Electroencephalogram (EEG) data collected at age 4 in predicting academic achievement at age 8 among rural children in Pakistan. Demographic and EEG data from 96 children of a cohort along with their academic achievement in grade 1–2 measured using an academic achievement test of Math and language at the age of 7–8 years was used to develop the machine learning algorithm. K- Nearest Neighbor (KNN) classifier was used on different model combinations of EEG, sociodemographic and home environment variables. KNN model was evaluated using 5 Stratified Folds based on the sensitivity and specificity. In the current dataset, 55% and 74% failed in the mathematics and language test respectively. On testing data across each fold, the mean sensitivity and specificity was calculated. Sensitivity was similar when EEG variables were combined with sociodemographic, and home environment (Math = 58.7%, Language = 66.3%) variables but specificity improved (Math = 43.4% to 50.6% and Language = 32% to 60%). The model requires further validation for EEG to be used as a screening measure with adequate sensitivity and specificity to identify children in their preschool age who may be at high risk of failure in early grades.

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Section: Plos Onementioning

confidence: 99%

Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan

Rasheed

Chand

Ahmed³

et al. 2021

PLoS ONE

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“…And compared with those of healthy peers, an increase in extremely high-amplitude spinous and slow waves in the high Rolandic region can be observed in the awake period, along with a widespread rhythmic outbreak of 2-3 Hz high-amplitude spinous and slow waves in the awake period. However, the discharge is significantly increased in the sleep period, and the spinous and slow wave discharge index is > 50% during the non-rapid-eye-movement sleep period[ 16 ]. In epilepsy accompanied by centrotemporal spikes, the presence of a status epilepticus EEG during sleep is known to cause nerve damage and cognitive changes; the higher the abnormal discharge index in the EEG, the more severe the cognitive damage in children.…”

Section: Discussionmentioning

confidence: 99%

Clinical and electroencephalogram characteristics and treatment outcomes in children with benign epilepsy and centrotemporal spikes

Chen¹,

Li²,

Wen³

et al. 2021

WJCC

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BACKGROUND Epilepsy is a syndrome characterized by transient, rigid, paroxysmal, and repetitive central nervous system dysfunction. Prevention, control, and improvement of cognitive and behavioral dysfunction are of great significance for improving the patients’ intellectual development and quality of life. Electroencephalograms (EEG) can predict an accelerated decline in cognitive function. AIM To determine the clinical and EEG characteristics and treatment results of benign epilepsy in spiking children. METHODS A total of 106 cases of benign epilepsy in children with myocardial spines treated at our hospital from January 2017 to January 2020 were selected. Differences in clinical data and EGG characteristics between treatment-effective/-ineffective patients were analyzed, and children’s intellectual development before and after treatment evaluated using the Gesell Development Diagnostic Scale. RESULTS EEG showed that the discharge proportion in the awake and sleep periods was 66.04%, and the peak/peak discharge was mainly single-sided, accounting for 81.13%, while the discharge generalization accounted for 31.13%. There was no significant difference in any of these variables between sexes and ages ( P > 0.05). The proportion of patients with early onset (< 5 years old) and seizure frequency > 3 times/half a year was 40.00% and 60.00%, respectively; the incidence rate and seizure frequency in the younger age group (< 5 years old) were significantly higher than those in the treatment-effective group ( P < 0.05), while the discharge index was significantly lower than that in the treatment-effective group ( P < 0.05). The discharge index was negatively correlated with fine motor skill and language development ( r = -0.274 and -0.247, respectively; P < 0.05), but not with the rest ( P > 0.05). Logistic regression analysis showed that low age onset (< 5 years old) and seizure frequency were the factors affecting ineffective-treatment of benign epilepsy in children (odds ratio = 11.304 and 5.784, respectively; P < 0.05). The discharge index of the responsive group after treatment was significantly lower than that of the unresponsive group ( P < 0.05). However, there was no significant difference between groups after treatment in gross and fine motor skills, adaptability, language, and personal social development ( P > 0.05). CONCLUSION The EEG of children with benign epilepsy due to spinal wave in central time zone has characteristic changes, and the therapeutic effect is influenced by age of onset and attack frequency.

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“…In humans with epilepsy both differences in absolute and relative power of the frequency spectrum have been found [ 55 ]. In children, epilepsy was found to be highly correlated with changes in the alpha band [ 56 ]. A study from Maheshwari, Marks [ 57 ] suggests that relative gamma power may serve as a biomarker of AED efficacy in absence epilepsy; drugs that reduce interictal gamma power increase the incidence of absence seizures.…”

Section: Discussionmentioning

confidence: 99%

Cannabinoid antagonist SLV326 induces convulsive seizures and changes in the interictal EEG in rats

et al. 2017

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Cannabinoid CB1 antagonists have been investigated for possible treatment of e.g. obesity-related disorders. However, clinical application was halted due to their symptoms of anxiety and depression. In addition to these adverse effects, we have shown earlier that chronic treatment with the CB1 antagonist rimonabant may induce EEG-confirmed convulsive seizures. In a regulatory repeat-dose toxicity study violent episodes of “muscle spasms” were observed in Wistar rats, daily dosed with the CB1 receptor antagonist SLV326 during 5 months. The aim of the present follow-up study was to investigate whether these violent movements were of an epileptic origin. In selected SLV326-treated and control animals, EEG and behavior were monitored for 24 hours. 25% of SLV326 treated animals showed 1 to 21 EEG-confirmed generalized convulsive seizures, whereas controls were seizure-free. The behavioral seizures were typical for a limbic origin. Moreover, interictal spikes were found in 38% of treated animals. The frequency spectrum of the interictal EEG of the treated rats showed a lower theta peak frequency, as well as lower gamma power compared to the controls. These frequency changes were state-dependent: they were only found during high locomotor activity. It is concluded that long term blockade of the endogenous cannabinoid system can provoke limbic seizures in otherwise healthy rats. Additionally, SLV326 alters the frequency spectrum of the EEG when rats are highly active, suggesting effects on complex behavior and cognition.

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Correlation of EEG with neuropsychological status in children with epilepsy

Cited by 7 publications

References 55 publications

Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan

Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan

Clinical and electroencephalogram characteristics and treatment outcomes in children with benign epilepsy and centrotemporal spikes

Cannabinoid antagonist SLV326 induces convulsive seizures and changes in the interictal EEG in rats

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