Gelastic seizure (GS) is a type of epilepsy characterized primarily by inappropriate bursts of laughter, with or without other epileptic events. Based on the timing of symptoms, the presence of emotional changes, and disturbances of consciousness, GS is classified into simple and complex types. The generation of laughter involves two major neural pathways: the emotional pathway and the volitional pathway. The neural network involved in GS includes structures such as the frontal lobe, insula, cingulate gyrus, temporal lobe, and brainstem.The most common cause of GS is a hypothalamic hamartoma, and stereotactic electroencephalography can record discharges from the lesion itself. Surgical removal of the hypothalamic hamartoma can result in immediate cessation of GS in the majority of patients, while some may experience partial improvement with persistent epileptic-like discharges detectable on scalp electroencephalography (EEG). Early surgical intervention may improve prognosis.In cases of non-hypothalamic origin of GS with no apparent imaging abnormalities, focal discharges are often observed on EEG and these cases respond well to antiepileptic drugs. Conversely, patients with structural abnormalities suggested by imaging studies tend to have multifocal discharges and a poorer response to medication. In a small subset of medically refractory non-hypothalamic GS, surgical intervention can effectively control symptoms.This article provides a comprehensive review of the etiology, neural networks involved, EEG characteristics, and treatment options for GS, with the goal of improving understanding of this relatively rare type of epileptic seizure.
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.
Purpose To analyze the clinical characteristicsand prognostic factors of Status epilepticus (SE) in children. Methods The clinical data of 33 children with SE treated in Jinan Central Hospital Affiliated of Shandong University from January 2014 to June 2021 were collected, and their clinical characteristics were analyzed. Then, according to Glasgow prognosis scale, the children were divided into good prognosis group (n=20) and poor prognosis group (n=13). The age of first attack, duration of attack, type of attack and SE classification, EEG, cranial imaging and etiology were used to analyze the influencing factors of SE prognosis. Results 75.7% were 0 ~ 6 years old in the age of first attack, and 29 cases of convulsive status epilepticus accounted for 87.9% in the classification of seizure types. There were significant differences in age of first attack, duration of attack, EEG, history of mental retardation and etiology between the two groups (P<0.05); Logistic regression analysis showed that the age of first attack, duration of attack, history of mental retardation and EEG were independent factors affecting the prognosis. Conclusion Low age, especially ≤ 6 years old, is the high incidence of SE in children at first attack. Most children are symptomatic and have obvious incentives. Convulsive SE is the main type of SE in children. The age of first onset, duration of epilepsy, history of mental retardation, and EEG can affect the prognosis of SE.
The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.
As an emerging non-invasive brain stimulation technique, transcranial direct current stimulation (tDCS) has received increasing attention in the field of stroke disease rehabilitation. However, its efficacy needs to be further studied. The tDCS has three stimulation modes: bipolar-stimulation mode, anode-stimulation mode and cathode-stimulation mode. Nineteen stroke patients were included in this research (10 with left-hemisphere lesion and 9 with right). Resting electroencephalogram (EEG) signals were collected from subjects before and after bipolar-stimulation, anodal-stimulation, cathodal-stimulation, and pseudo-stimulation, with pseudo-stimulation serving as the control group. The changes of multi-scale intrinsic fuzzy entropy (MIFE) of EEG signals before and after stimulation were compared. The results revealed that MIFE was significantly greater in the frontal and central regions after bipolar-stimulation (P < 0.05), in the left central region after anodal-stimulation (P < 0.05), and in the frontal and right central regions after cathodal-stimulation (P < 0.05) in patients with left-hemisphere lesions. MIFE was significantly greater in the frontal, central and parieto-occipital joint regions after bipolar-stimulation (P < 0.05), in the left frontal and right central regions after anodal- stimulation (P < 0.05), and in the central and right occipital regions after cathodal-stimulation (P < 0.05) in patients with right-hemisphere lesions. However, the difference before and after pseudo-stimulation was not statistically significant (P > 0.05). The results of this paper showed that the bipolar stimulation pattern affected the largest range of brain areas, and it might provide a reference for the clinical study of rehabilitation after stroke.
ObjectiveTo analyze and summarize the clinical and video EEG (VEEG) characteristics of tuberous sclerosis (TSC) with epilepsy.MethodsClinical data of 30 children with TSC who met the revised diagnostic criteria of TSC in 2012 from Jan. 2016 to May 2019 in Zhengzhou Children’s Hospital were collected, including 29 children with epileptic seizures. The characteristics of skin lesions, imaging, seizures and long-term VEEG were analyzed retrospectively.ResultsThe mean age was (2.88 ± 2.64), 12 males and 18 females, 1 case of lumbar acid as the first symptom, 29 cases with epilepsy as the first symptom, the incidence of epilepsy is high, and the onset age is less than 1 year old; TSC can cause different degrees of cognitive impact; depigmentation or milk coffee spots are the most common skin changes in young children; TSC with infantile spasm has a high incidence; children younger than 10 years old may have lesions of other organs except nervous system lesions. However, the incidence of other organ lesions was relatively low. Most of TSC children with epilepsy were accompanied by abnormal EEG discharge.ConclusionThe clinical characteristics of TSC with epileptic seizures are various, and early diagnosis is of great significance.
Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model—calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern (wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.
ObjectiveTo probe the clinical features and the characteristics of radiography and electroencephalogram (EEG) of tuberous sclerosis complex(TSC) in children with epilepsy. MethodsThe clinical data of the TSC cases with epilepsy were collected from inpatients in Jiangxi Children's Hospital from Jan. 2013 to Oct. 2015. ResultsAmong the 26 cases, 21 cases(21/26, 80.77%) involved abnormalities of the skin. Of these patients, there were 10 cases with hypomelanotic macules, 7 cases with café au lait spots and 4 cases with facial angiofibromas. There were no significant difference among the different age groups. In addition, there were 8 cases (8/26, 30.77%) with spasm seizures, of whom 3 cases had partial seizure, 10 cases (10/26, 38.46%) with complex partial seizure, 5 cases(5/26, 19.23%) with secondary generalized seizure, 2 cases(2/26, 7.69%) with tonic-clonic seizure and one case with Lennox-Gastaut syndrom(1/26, 3.85%). The average onset age of the epileptic spasms group were younger than those of the other epilepsy groups (t=2.143, P=0.042). EEG monitoring demonstrated hypsarrhythmia in 7 cases (7/26, 26.92%) in the interictal EEG, focal epileptic discharges in 11 cases (11/26, 42.31%), multifocal discharges in 5 cases, the slow background activity in 2 cases and the normal EEG in one case. Cranial imaging demonstrated subependymal nodules (SEN) in 25 cases(25/26, 96.15%) was the most common. ConclusionThe clinical manifestations and seizure types of TSC in children, especially in infants and young children, were diverse and age-dependent. It was very important to improve understanding of the clinical features and related risks of TSC at various ages, which was helpful to diagnose TSC early.
ObjectiveTo analyze the effect of mitochondrial ultrastructural changes caused by morphine toxicity on abnormal discharge of cat cerebral cortex, and to explore the possible mechanism of brain function damage caused by morphine dependence.MethodsTwelve domestic cats were divided into control group (3 cats) and morphine exposed group (9 cats) according to the method of random number table. After the model was successfully established by the method of dose increasing, the changes of mitochondrial ultrastructure of cortical neurons were observed under the electron microscope.ResultsElectroencephalogram (EEG) monitoring in morphine exposed group showed that the cortical EEG was widely abnormal, physiological waves were reduced, and abnormal discharges were frequent. And the electron microscopy showed that the number, morphology, internal membrane structure and the inclusion body in the matrix of neurons changed in various aspects. The EEG and electron microscopy of the control group were normal.ConclusionMorphine can damage neurons in the cerebral cortex and lead to abnormal discharge, which is closely related to the ultrastructural changes of neuron mitochondria. The toxicity of morphine mitochondria can be the initial mechanism of energy metabolism dysfunction of brain cells and eventually lead to the disorder of brain electrophysiological function.