Risk for developing epilepsy and epileptiform discharges on EEG in patients with febrile seizures

Purpose: The aim of the present study was to investigate the correlation between epileptiform discharges on EEGs after febrile seizures and the prognosis of patients in terms of the development of epilepsy and recurrence of febrile seizures. This study also evaluated the characteristics of epileptiform discharges and EEG changes on follow-up examination. Methods: This study consisted of 36 children who presented to our hospital with febrile seizures and whose electroencephalograms (EEG) showed epileptiform discharges. The development of epilepsy and the recurrence of febrile seizures were compared between the study group (n = 36) and the control group (n = 87), which included children with febrile seizure but with normal EEG findings. Results: No significant correlation was detected between the recurrence rate of febrile seizures in patients with normal EEG (23 out of 87, 26.4%) findings and that of patients whose EEGs showed epileptiform discharges (12 out of 36, 33.3%) [adjusted OR 0.67 (0.26–1.68)]. However, 9 (25.0%) out of 36 patients with epileptiform discharges on EEG had epilepsy compared to 2 patients (2.3%) in the control group. The correlation was statistically significant [crude OR 10.88 (2.47–47.88) and adjusted OR 8.75 (1.49–51.6)]. Conclusion: Epileptiform discharges on the EEGs of patients with febrile seizures are important predictive risk factors of the development of epilepsy.     

深部脑电图在七氟醚麻醉条件下的(痫)样放电波幅变异

目的:比较七氟醚在不同麻醉深度下对颞叶癫(痫)手术术中深部电极描记的EEG的影响.方法:颞叶癫(痫)行射频热凝毁损手术患者68例,在七氟醚吸入麻醉,最小肺泡浓度(MAC) =0.6时监测额叶皮质EEG和颞叶深部EEG,再加深麻醉至MAC=1.2时再监测.EEG数据应用快速傅里叶处理(FFT),计算额叶背景平均波幅,测量10个颞叶内侧棘波的波幅,取平均值后确定为该患者的棘波波幅,对MAC=0.6和MAC=1.2两组进行统计.结果:MAC=0.6时颞叶内侧棘波放电波幅平均为(426.2±63.1)μV,额叶10～12 Hz(80.3±16.4)μV背景波幅显著;MAC=1.2时颞叶内侧棘波放电波幅平均为(171.2±32.6)μV,额叶10～12 Hz(550.3±126.8)μV背景波幅显著,两组间的(痫)样放电波幅和额叶背景波幅比较差异均有统计学意义(P＜0.05).MAC≤1.2时,七氟醚吸入麻醉影响背景节律和(痫)样放电的波幅,对频率和波形无明显影响.结论:七氟醚麻醉对深部电极EEG的影响呈剂量依赖性,麻醉过深则可能导致(痫)样放电鉴别困难.     

Deep learning for detection of focal epileptiform discharges from scalp EEG recordings

Objective

Visual assessment of the EEG still outperforms current computer algorithms in detecting epileptiform discharges. Deep learning is a promising novel approach, being able to learn from large datasets. Here, we show pilot results of detecting epileptiform discharges using deep neural networks.

Reduction of epileptiform activity in response to low-dose clonazepam in children with epilepsy: a randomized double-blind study

PURPOSE: To evaluate the effect of low-dose clonazepam (CZP) on the amount of epileptiform activity in children with focal and generalized epilepsy. METHODS: In a single-blind pilot study, followed by a double-blind, placebo-controlled, randomized, crossover study, 15 children with epilepsy were evaluated by using 24-h long-term EEG recordings during baseline days and days after injections of placebo and CZP. The drug was given as a single i.m. injection of 0.02 mg/kg BW. Blood samples were obtained regularly for analysis of plasma concentrations of CZP. The number of epileptiform discharges was determined during corresponding periods with the individual child in the same state of alertness, the same real time of day, and with concomitant antiepileptic drugs (AEDs) unchanged. RESULTS: In the double-blind study, low-dose CZP produced a highly significant (p = 0.0015) decrease in the amount of epileptiform activity (mean, -69% vs. placebo, -2%) obtained during periods when median plasma concentrations ranged from 18 to <14 nM. The maximal plasma level (median, 24 nM) was reached before the start of the analysis periods. The pilot study showed reductions of epileptiform discharges within the same range as the double-blind study. In the children with daily seizures, a parallel decrease in seizures and the number of epileptiform discharges was seen after the administration of CZP. CONCLUSIONS: Our data demonstrate a significant reduction of epileptiform discharges on long-term EEGs after a single low dose of CZP with concomitant low plasma levels, which were considerably lower than the doses and plasma levels usually recommended. A concomitant reduction of seizures also was seen.     

EEG autoregressive modeling analysis: A diagnostic tool for patients with epilepsy without epileptiform discharges

ObjectiveNumerous types of nonepileptic paroxysmal events, such as syncopes and psychogenic nonepileptic seizures, may imitate epileptic seizures and lead to diagnostic difficulty. Such misdiagnoses may lead to inappropriate treatment in patients that can considerably affect their lives. Electroencephalogram (EEG) is a commonly used tool in assisting diagnosis of epilepsy. Although the appearance of epileptiform discharges (EDs) in EEG recordings is specific for epilepsy diagnosis, only 25%–56% of patients with epilepsy show EDs in their first EEG examination.MethodsIn this study, we developed an autoregressive (AR) model prediction error–based EEG classification method to distinguish EEG signals between controls and patients with epilepsy without EDs. Twenty-three patients with generalized epilepsy without EDs in their EEG recordings and 23 age-matched controls were enrolled. Their EEG recordings were classified using AR model prediction error–based EEG features.ResultsAmong different classification methods, XGBoost achieved the highest performance in terms of accuracy and true positive rate. The results showed that the accuracy, area under the curve, true positive rate, and true negative rate were 85.17%, 87.54%, 89.98%, and 81.81%, respectively.ConclusionsOur proposed method can help neurologists in the early diagnosis of epilepsy in patients without EDs and might help in differentiating between nonepileptic paroxysmal events and epilepsy.SignificanceEEG AR model prediction errors could be used as an alternative diagnostic marker of epilepsy.     

Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis

ObjectiveTo quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns.MethodsWe analyzed long-term EEGs from 76 patients with at least one recorded epileptic seizure during monitoring. IEDs were detected with an AI-based algorithm and validated by visual inspection. We then used unsupervised clustering to characterize patient sub-cohorts with similar IED activation patterns regarding circadian rhythms, deep sleep activation, and seizure occurrence.ResultsFive sub-cohorts with similar IED activation patterns were found: “Sporadic” (14%, n = 10) without or few IEDs, “Continuous” (32%, n = 23) with weak circadian/deep sleep or seizure modulation, “Nighttime & seizure activation” (23%, n = 17) with high IED rates during normal sleep times and after seizures but without deep sleep modulation, “Deep sleep” (19%, n = 14) with strong IED modulation during deep sleep, and “Seizure deactivation” (12%, n = 9) with deactivation of IEDs after seizures. Patients showing “Deep sleep” IED pattern were diagnosed with temporal lobe epilepsy in 86%, while 80% of the “Sporadic” cluster were extratemporal.ConclusionsPatients with epilepsy can be characterized by using temporal relationships between rates of IEDs, circadian rhythms, deep sleep and seizures.SignificanceThis work presents the first approach to data-driven classification of epilepsy patients based on their fully validated temporal pattern of IEDs.     

临床下痫样放电对青少年癫痫患者认知功能影响的研究

目的探讨临床下痛样放电对青少年癫痫患者认知功能的影响。方法以全身强直阵挛性发作（GTCS）为临床表现的特发性癫痫患者65例,无临床发作均超过3个月,其中35例动态脑电图或普通脑电图显示有痫样放电,30例患者动态脑电图均正常。65例患者均服用左乙拉西坦治疗,随访6个月,服药前后分别进行基本认知能力测试,分析两组患者6个月前后认知功能的变化。结果63例完成本实验,两组实验前后IQ均在正常范围且差异不明显。但6个月后有痛样放电组认知功能明显下降（P〈0．01）,具体表现在数字鉴别（P〈0．01）、汉字快速比较（P〈0．01）、汉字旋转（P〈0．01）、图形再认（P〈0．01）等方面,而脑电图正常组认知功能无明显变化（P〉0．05）。结论临床下痫样放电可损害患者的认知能力,应受到重视,采取适当的干预措施。     

Isolation of epileptiform discharges from unaveraged EEG by independent component analysis.

OBJECTIVE: We propose a method that allows the separation of epileptiform discharges (EDs) from the EEG background, including the ED's waveform and spatial distribution. The method even allows to separate a spike in two components occurring at approximately the same time but having different waveforms and spatial distributions. METHODS: The separation employs independent component analysis (ICA) and is not based on any assumption regarding generator model. A simulation study was performed by generating ten EEG data matrices by computer: each matrix included real background activity from a normal subject to which was added an array of simulated unaveraged EDs. Each discharge was a summation of two transients having slightly different potential field distributions and small jitters in time and amplitude. Real EEG data were also obtained from three epileptic patients. RESULTS: Through ICA, we could isolate the two epileptiform transients in every simulation matrix, and the retrieved transients were almost identical as the originals, especially in their spatial distributions. Two epileptic components were isolated by ICA in all patients. Each estimated epileptic component had a consistent time course. CONCLUSION: ICA appears promising for the separation of unaveraged spikes from the EEG background and their decomposition in independent spatio-temporal components.