Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.
Humans ; Scalp ; Epilepsy/diagnosis* ; Brain ; Electroencephalography

There are some limitations in the localization of epileptogenic zone commonly used by human eyes to identify abnormal discharges of intracranial electroencephalography in epilepsy. However, at present, the accuracy of the localization of epileptogenic zone by extracting intracranial electroencephalography features needs to be further improved. As a new method using dynamic network model, neural fragility has potential application value in the localization of epileptogenic zone. In this paper, the neural fragility analysis method was used to analyze the stereoelectroencephalography signals of 35 seizures in 20 patients, and then the epileptogenic zone electrodes were classified using the random forest model, and the classification results were compared with the time-frequency characteristics of six different frequency bands extracted by short-time Fourier transform. The results showed that the area under curve (AUC) of epileptic focus electrodes based on time-frequency analysis was 0.870 (delta) to 0.956 (high gamma), and its classification accuracy increased with the increase of frequency band, while the AUC by using neural fragility could reach 0.957. After fusing the neural fragility and the time-frequency characteristics of the γ and high γ band, the AUC could be further increased to 0.969, which was improved on the original basis. This paper verifies the effectiveness of neural fragility in identifying epileptogenic zone, and provides a theoretical reference for its further clinical application.
Humans ; Electroencephalography/methods* ; Epilepsy/diagnosis* ; Seizures ; Stereotaxic Techniques

Objective: To summarize the clinical characteristics of epileptic seizure associated with neurofibromatosis type 1 (NF1). Methods: From January 2017 to July 2023 at Children's Hospital Capital Institute of Pediatrics, medical records of patients with both NF1 and epileptic seizure were reviewed in this case series study. The clinical characteristics, treatment and prognosis were analyzed retrospectively. Results: A total of 15 patients(12 boys and 3 girls) were collected. Café-au-lait macules were observed in all 15 patients. There were 6 patients with neurodevelopmental disorders and the main manifestations were intellectual disability or developmental delay. The age at the first epileptic seizure was 2.5 (1.2, 5.5) years. There were various seizure types, including generalized tonic-clonic seizures in 8 patients, focal motor seizures in 6 patients, epileptic spasm in 4 patients, tonic seizures in 1 patient, absence in 1 patient, generalized myoclonic seizure in 1 patient and focal to bilateral tonic-clonic seizure in 1 patient. Among 14 patients whose brain magnetic resonance imaging results were available, there were abnormal signals in corpus callosum, basal ganglia, thalamus or cerebellum in 6 patients, dilated ventricles of different degrees in 3 patients, blurred gray and white matter boundary in 2 patients, agenesis of corpus callosum in 1 patient and no obvious abnormalities in the other patients. Among 13 epilepsy patients, 8 were seizure-free with 1 or 2 antiseizure medications(ASM), 1 with drug resistant epilepsy was seizure-free after left temporal lobectomy, and the other 4 patients who have received 2 to 9 ASM had persistent seizures. One patient with complex febrile convulsion achieved seizure freedom after oral administration of diazepam on demand. One patient had only 1 unprovoked epileptic seizure and did not have another seizure without taking any ASM. Conclusions: The first epileptic seizure in NF1 patients usually occurs in infancy and early childhood, with the main seizure type of generalized tonic-clonic seizure and focal motor seizure. Some patients have intellectual disability or developmental delay. Most epilepsy patients achieve seizure freedom with ASM.
Male ; Female ; Humans ; Child, Preschool ; Child ; Neurofibromatosis 1/diagnosis* ; Retrospective Studies ; Intellectual Disability ; Electroencephalography ; Epilepsy/etiology* ; Seizures/etiology*

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.
Humans ; Scalp ; Brain Mapping/methods* ; Epilepsy/diagnosis* ; Electroencephalography/methods* ; Brain

OBJECTIVE: To explore the clinical and genetic characteristics of two children with Williams-Beuren syndrome (WBS). METHODS: Two children who had presented at the Department of Pediatrics, General Hospital of Ningxia Medical University respectively on January 26 and March 18, 2021 were selected as the study subjects. Clinical data and results of genetic testing of the two patients were analyzed. RESULTS: Both children had featured developmental delay, characteristic facies and cardiovascular malformation. Child 1 also had subclinical hypothyroidism, whilst child 2 had occurrence of epilepsy. Genetic testing revealed that child 1 has harbored a 1.54 Mb deletion in the 7q11.23 region, whilst child 2 has a 1.53 Mb deletion in the same region, in addition with a c.158G>A variant of the ATP1A1 gene and a c.12181A>G variant of the KMT2C gene. Based on the guidelines from the American College of Medical Genetics and Genomics, the c.158G>A and c.12181A>G variants were rated as variants of unknown significance (PM1+PM2_Supporting+PP2+PP3；PM2_Supporting). CONCLUSION Both children had characteristic features of WBS, for which deletions of the 7q11.23 region may be accountable. For children manifesting developmental delay, facial dysmorphism and cardiovascular malformations, the diagnosis of WBS should be suspected, and genetic testing should be recommended to confirm the diagnosis.
Child ; Humans ; Williams Syndrome/diagnosis* ; Genetic Testing ; Facies ; Epilepsy/genetics* ; Chromosomes, Human, Pair 7/genetics* ; Chromosome Deletion

Humans ; Diagnosis, Differential ; Long QT Syndrome/genetics* ; Syncope/diagnosis* ; Epilepsy/diagnosis* ; Electrocardiography

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.
Algorithms ; Child ; Deep Learning ; Electroencephalography ; Epilepsy/diagnosis* ; Humans ; Seizures/diagnosis* ; Signal Processing, Computer-Assisted ; Wavelet Analysis

Neonatal seizures are the most common clinical manifestations of critically ill neonates and often suggest serious diseases and complicated etiologies. The precise diagnosis of this disease can optimize the use of anti-seizure medication, reduce hospital costs, and improve the long-term neurodevelopmental outcomes. Currently, a few artificial intelligence-assisted diagnosis and treatment systems have been developed for neonatal seizures, but there is still a lack of high-level evidence for the diagnosis and treatment value in the real world. Based on an artificial intelligence-assisted diagnosis and treatment systems that has been developed for neonatal seizures, this study plans to recruit 370 neonates at a high risk of seizures from 6 neonatal intensive care units (NICUs) in China, in order to evaluate the effect of the system on the diagnosis, treatment, and prognosis of neonatal seizures in neonates with different gestational ages in the NICU. In this study, a diagnostic study protocol is used to evaluate the diagnostic value of the system, and a randomized parallel-controlled trial is designed to evaluate the effect of the system on the treatment and prognosis of neonates at a high risk of seizures. This multicenter prospective study will provide high-level evidence for the clinical application of artificial intelligence-assisted diagnosis and treatment systems for neonatal seizures in the real world.
Artificial Intelligence ; Electroencephalography/methods* ; Epilepsy/diagnosis* ; Humans ; Infant, Newborn ; Infant, Newborn, Diseases/diagnosis* ; Intensive Care Units, Neonatal ; Multicenter Studies as Topic ; Prospective Studies ; Randomized Controlled Trials as Topic ; Seizures/drug therapy*

At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.
Algorithms ; Electroencephalography ; Epilepsy/diagnosis* ; Humans ; Signal Processing, Computer-Assisted ; Support Vector Machine

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.
Algorithms ; Electroencephalography ; Epilepsy/diagnosis* ; Humans ; Seizures/diagnosis* ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wavelet Analysis