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.
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.
Objective To understand the status quo of medical staffs engaged in epilepsy and EEG in Shanxi Province, analyze the existing problems, and summarize the improvement and development direction of epilepsy and EEG in Shanxi Province. Methods A questionnaire survey was conducted among medical staff of epilepsy and electroencephalogram specialty in public hospitals at or above county level in whole province and municipalities. Results ① Generally speaking, there are 17 males and 473 females in this study, with an average age of 38.7 years, the youngest was 23 years-old and the oldest was 70 years-old; ② The regional distribution has a tendency of decrease from Taiyuan in Shanxi Province to the remote areas of southeast, northwest and northwest China, and the epilepsy treatment in some poverty-stricken areas have not even been carried out; ③ The shortest time of working is 3 months and the longest is more than 40 years. The proportion of junior collage students, undergraduates, masters and doctors is 24%, 50%, 25% and 1% respectivel. The professional titles of primary, medium-level, vice-senior and senior are 24%, 39%, 26% and 11% respectively. Conclusion The number of medical workers engaged in EEG specialty in Shanxi Province is insufficient, the regional development is not balanced, and the number of junior and medium-level professional titles is large. We can formulate a mobile policy to encourage experienced medical personnel to communicate with weak areas, so as to improve the overall level of epilepsy and EEG professional development in Shanxi Province.
At present, the incidence of Parkinson’s disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band (P = 0.034) and State5 of Gamma frequency band (P = 0.010) could be used to differentiate health controls and off-medication Parkinson’s disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson’s disease.
The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.
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.
Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.
ObjectiveThe purpose of the research is to study the distribution and early warning of electroencephalogram (EEG) in acute mountain sickness (AMS). MethodsA total of 280 healthy young men were recruited from September 2016 to October 2016. The basic data were collected by the centralized flow method, the general situation of the division of the investigators after the training, the Lewis Lake score, the computer self-rating anxiety scale and depression scale, and the collection of EEG. Follow up in three months. Results94 of the patients with AMS, morbidity is 33%, 21 (22.34%) of the patients are moderate to severe, 73 (77.66%) are mild, morbidity is 26.67%. The abnormal detection rate of electrogram was 7.9% (22/280), which were mild EEG, normal EEG abnormal rate was 8.6% (16/186), abnormal detection rate of mild AMS was 4.1% (3/73), and the abnormal detection rate was 14.3% (3/21) in the medium / heavy AMS. The latter was significantly different from the previous (P < 0.05). Three months follow-up of this group of patients with 0 case of high altitude disease. Conclusions The EEG in AMS is mainly a rhythm irregular, unstable, poor amplitude modulation; or two hemisphere volatility difference of more than 50% or slightly increased activity. The result is statistically significant, suggesting that EEG distributions has possible early warning of AMS.
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.
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.