The electroencephalogram (EEG) has proved to be a valuable tool in the study of comprehensive conditions whose effects are manifest in the electrical brain activity, and epilepsy is one of such conditions. In the study, multi-scale permutation entropy (MPE) was proposed to describe dynamical characteristics of EEG recordings from epilepsy and healthy subjects, then all the characteristic parameters were forwarded into a support vector machine (SVM) for classification. The classification accuracies of the MPE with SVM were evaluated by a series of experiments. It is indicated that the dynamical characteristics of EEG data with MPE could identify the differences among healthy, inter-ictal and ictal states, and there was a reduction of MPE of EEG from the healthy and inter-ictal state to the ictal state. Experimental results demonstrated that average classification accuracy was 100% by using the MPE as a feature to characterize the healthy and seizure, while 99.58% accuracy was obtained to distinguish the seizure-free and seizure EEG. In addition, the single-scale permutation entropy (PE) at scales 1-5 was put into the SVM for classification at the same time for comparative analysis. The simulation results demonstrated that the proposed method could be a very powerful algorithm for seizure prediction and could have much better performance than the methods based on single scale PE.
Modified electroconvulsive therapy (MECT) and magnetic seizure therapy (MST) are effective treatments for severe major depression. MECT has better efficacy in the treatment than MST, but it has cognitive and memorial side effects while MST does not. To study the causes of these different outcomes, this study contrasted the electric filed strength and spatial distribution induced by MECT and MST in a realistic human head model. Electric field strength induced by MECT and MST are simulated by the finite element method, which was based on a realistic human head model obtained by magnetic resonance imaging. The electrode configuration of MECT is standard bifrontal stimulation configuration, and the coil configuration of MST is circular. Maps of the ratio of the electric field strength to neural activation threshold are obtained to evaluate the stimulation strength and stimulation focality in brain regions. The stimulation strength induced by MECT is stronger than MST, and the activated region is wider. MECT stimulation strength in gray matter is 17.817 times of that by MST, and MECT stimulation strength in white matter is 23.312 times of that by MST. As well, MECT stimulation strength in hippocampi is 35.162 times of that by MST. More than 99.999% of the brain volume is stimulated at suprathreshold by MECT. However, MST activated only 0.700% of the brain volume. The stimulation strength induced by MECT is stronger than MST, and the activated region is wider may be the reason that MECT has better effectiveness. Nevertheless, the stronger stimulation strength in hippocampi induced by MECT may be the reason that MECT is more likely to give rise to side effects. Based on the results of this study, it is expected that a more accurate clinical quantitative treatment scheme should be studied in the future.
Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.
Febrile seizure is one of the most common emergencies in children, accounting for about 30% of all types of children, and the most common among children aged 6 months to 5 years. At the same time, children in this age group are at the peak of growth and development, and the content of various trace elements in the body is prone to abnormalities. At present, there are few related studies on febrile seizure and trace elements in children. This paper summarizes the related studies on febrile seizure and trace elements in order to provide theoretical guidance for the prevention and treatment of febrile seizure
ObjectiveTo explore the effects of cytokines on Febrile seizures (FS) in children with febrile seizures (Febrile seizures), febrile seizures duration and prognosis, and to explore the correlation between cytokines and the clinical manifestations and prognosis of FS. MethodsA retrospective analysis was performed on 121 children with FS (77 cases in the simple FS group and 44 cases in the complex FS group) who were treated in the pediatrics department of the Maternal and Child Health Hospital of Inner Mongolia Autonomous Region from January 2021 to October 2022 as the experimental group, including 71 males and 50 females, with a male-to-female ratio of 1.42:1, according to the type of attack (93 cases in the comprehensive group, 44 cases in the complex FS group). The focal group (28 cases) and convulsion duration (91 cases in <5 min group and 30 cases in ≥5 min group) were divided into groups, and 127 cases of children with fever but no convulsions were compared with the control group. In addition, 121 children with FS were followed up for 1 year by neurology specialist outpatient department and telephone follow-up. According to the follow-up, they were divided into the first course group, the relapse group and the secondary epilepsy group, so as to further explore the correlation between cytokines and the prognosis of children with FS. ResultsExperimental group compared with control group: Serum IL-1β (1.38 pg/mL), IL-2 (2.26 pg/mL), IL-4 (1.53 pg/mL), IL-6 (10.51 pg/mL), IL-10 (3.09 pg/mL), IL-12p70 (1.74 pg/mL), TNF-α (2.11 pg/mL), IFN-γ (46.56 pg/mL), IL-1β (1.38 pg/mL), IL-1β (1.26 pg/mL), IL-4 (1.53 pg/mL), IL-6 (10.51 pg/mL), IL-10 (3.09 pg/mL), IL-12P70 (1.74 pg/mL), TNF-α (2.11 pg/mL), IFN-γ (46.56 pg/mL). IFN-α (25.92 pg/mL) levels were higher, and the differences were statistically significant (P<0.05). There was no significant difference between the simple group and the complex group (P>0.05). <5 min group compared with control group: serum levels of IL-2 (2.32 pg/mL), IL-4 (1.53 pg/mL), IL-6 (9.65 pg/mL), IL-12p70 (1.74 pg/mL), TNF-α (2.11 pg/mL), IFN-γ (44.63 pg/mL), IFN-α (29.67 pg/mL) were higher, and the differences were statistically significant (P<0.05). Compared with control group, the levels of IL-2 (2.06 pg/mL), IL-6 (14.67 pg/mL), IL-12p70 (1.97 pg/mL), IFN-γ (58.56 pg/mL) and IFN-α (17.50 pg/mL) in ≥5 min group were higher, and the differences were statistically significant (P<0.05). ROC curve analysis showed that serum IFN-α had a high predictive value for FS onset, the cut-off point was 8.64pg/ml, and the sensitivity and specificity were 75.63% and 76.38%, respectively. There was no significant difference between the first course of disease group, relapse group and secondary epilepsy group. ConclusionSerum proinflammatory cytokines IL-1β, IL-2, IL-6, IL-12p70, TNF-α, IFN-γ, IFN-α and anti-inflammatory cytokines IL-4 and IL-10 are involved in the pathogenesis of FS. There was no correlation between the simplicity and complexity of serum cytokines. IL-2, IL-6, IL-12p70, IFN-γ, IFN-α were positively correlated with the duration of convulsion. When serum IFN-α>8.64 pg/ml, the possibility of FS attack increased.
ObjectiveThe optimal target of deep brain stimulation (DBS) for treating intractable epilepsy is still undefined. Cumulative studies suggest that the mediodorsal thalamic nucleus (MD) is involved in seizure activity, the purpose of this study was to investigate the effect of high frequency stimulation in MD on pentylenetetrazole (PTZ)-induced seizures in rats.
MethodsThe experimental rats (Male Sprague-Dawley rats 280-350 g) were all provided by Experimental Animal Center, Zhejiang Academy of Medical Science, Hangzhou, China. The rats were given unilateral or bilateral stimulation of the MD at 100 Hz (HFS group) and sham stimulation, others were given unilateral stimulation of the MD at 1 Hz (LFS group). EEGs in the cortex and seizure behavior were recorded with the Neuroscan system at the same time.
ResultsNeither LFS nor HFS of the MD changed the latency to the first spikes or EEG manifestations for stage 3 and stage 5 seizures; animals receiving unilateral or bilateral HFS of the MD decreased the number of stage 5 EEG seizure synchronized with the convulsive episodes; LFS and sham stimulation showed multiple periods of continuous spikes which accompanied stage 5 or stage 4 seizures. HFS of unilateral or bilateral MD, but not LFS, decreased the seizure stage, the number of clonic movement episodes, and the duration of acute PTZ-induced seizures. The average latency to onset of myoclonic jerks did not differ among groups. Unilateral and bilateral HFS of the MD had a similar antiepileptic effect.
ConclusionHFS of the MD may be of value as a new antiepileptic approach for patients with generalized epilepsy, besides, the seizure model, should be fully considered in clinical application.
In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.
It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.
ObjectiveTo explore the clinical value of video-electroencephalograph (VEEG) for non-epileptic seizures disease in children.
MethodsThe clinical data of 58 children with non-epileptic seizures (NES) diagnosed by VEEG from October 2010 to November 2012 were retrospectively analyzed.
ResultsIn 50 out of 58 patients in the process of monitoring,the NES clinical onset was found while no synchronized epileptiform discharges was observed;in five patients with NES combined with epilepsy,no epileptiform discharges was found by VEEG at the clinical onset of NES;there were 3 patients with epileptiform discharges without seizures,who had no history of epilepsy,but non-synchronized clinical nonparoxysmal epileptiform discharges was found by VEEG monitoring.
ConclusionVEEG is an effective diagnosis method for NES and seizures in children,which could be regarded as the gold standard for NES diagnosis.
Genetic epilepsy with febrile seizures plus (GEFS+) is a new type of genetic epilepsy syndrome with a marked hereditary tendency. Febrile seizure is the most common clinical symptom, followed by febrile seizure plus, and with/without absence seizures, focal seizures, and generalized tonic-clonic seizures. Results of the polymerase chain reaction (PCR), exon sequencing and single nucleotide polymorphism (SNP) analysis showed that the occurrence of GEFS+ is mainly related to the mutation of gamma aminobutyric acid type A receptor gamma 2 subunit (GABRG2), but its pathogenesis was still unclear. The main types of GABRG2 mutations include missense mutation, nonsense mutation, frameshift mutation, point mutation and splice site mutation. All these types of mutations can reduce the function of ion channels on cell membrane, but the degree and mechanism of dysfunction are different, which may be the main mechanism of epilepsy. This article will focus on the relationship between GEFS+ and the mutation types of GABRG2 in recent years, which is of great significance for clinical accurate diagnosis, anti-epileptic treatment strategy and new drug development.
