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.
In the present paper, wavelet transform and empirical mode decomposition (EMD) are combined to extracted the features of electroencephalogram (EEG) signal with music intervention, and to achieve a better classification accuracy rate and reliability in emotional assessment in order to provide a support for music therapy. The data were from Database for Emotion Analysis using Physiological Signals (DEAP). Based on wavelet transform α, β and θ rhythms were extracted at frontal (F3, F4), temporal (T7, T8) and central regions (C3, C4). Based on the EMD, the intrinsic mode function (IMF) was analyzed and extracted. Furthermore, average energy and amplitude difference of IMF were analyzed and obtained. The support vector machine was used to assess the state of emotion in order to support music therapy. According to this algorithm, the classification accuracy rate could reach 100% between no emotions, positive emotions and negative emotions, which made a 10% improvement between positive and negative emotion recognition. Effective evaluation result between positive and negative emotions was achieved. The states of emotion would influence the effect of music therapy, undoubtedly, the classification accuracy rate increasing of emo-tional assessment will further help improve the effect of music therapy and provide a better support to the therapy.
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 improve the clinicians’ understanding and treatment level of electrical status epilepticus during sleep by analyzing the clinical data of patients with ESES retrospectively.MethodsWe collected 20 cases from 2018 to 2019 diagnosed in our hospital by clinical and 24-hour video EEG confirmed and analyzed the clinical manifestations, EEG features, seizure type, classification of epilepsy syndrome as well as the imaging findings.ResultsAmong the 20 patients, there were 12 males and 8 females, the mean age was (10.96±2.68) years old, and the first onset age was (8.90±1.93) years old. Epilepsy seizure as the first symptom is most common, the EEG showed a broad or localized sustained discharge, generalized tonic clonic seizure is the main form of seizure type in most patients, ECSWS is the most prominent in the epilepsy syndrome, for small lesion through MR can be found early.ConclusionMost patients have partial or generalized seizures, which have a range of cognitive impairment, mental decline or other issues. It is important to improve the cognitive function, behavior, neuropsychology of patients by long term video EEG monitoring in the early stage.
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.
ObjectiveTo investigate the lateralization of ictal scalp EEG in different times in focal epilepsy.Methods356 surface ictal EEG of 41 patients were reviewed retrospectively in focal epilepsy arising from the mesial frontal, lateralfrontal, mesialtemporal, neocorticaltemporal, insular lobes and posterior cortex from July, 2010 to at, 2016. Each ictal scalp EEG was subdivided into ten epoches (E1-E10), then the lateralization of every epoch was analyzed. Ten epochs EEG were merged into three timesas E1-E3, E4-E6 and E7-E10. The ratio of lateralization, mislateralization and non-lateralization of each timeEEG were studied. Ictal onset zone (IOZ) were precise localized by intracranial EEG. The results of epileptogenic zone corresponded with surgical outcomes as seizure free or decreased.Results62% seizures were lateralized by surface ictal EEG in all epilepsies. Lateralized ictal scalp EEG were seen in nearly 80% of seizures in all times in temporal lobe epilepsy (TLE). The highest lateralization of 89% occurred inE4-E6 andfalse lateralization up to 30% in E1-E3 in mesial temporal lobe epilepsy (MTLE), whereas 95% lateralized seizures emerged in E1-E3 in neocortical temporal lobe epilepsy (NTLE). Apparent non-lateralization in all times were higher than lateralization in frontal lobe epilepsy (FLE), especially in mesial frontal lobe epilepsy (MFLE). Lateralization in E1-E3 was only 24% higher than other times. In addition, False lateralization never occurred in all times in lateral frontal lobe epilepsy (LFLE). There were maximum of 83%lateralized seizures in E1-E3 in LFLE and 93% in E1-E3 in posterior cortex epilepsy (PCE). Seizures arising from insular lobe epilepsy (ILE) tendedto predict less lateralization in all times.ConclusionsIctal scalp EEG of E1-E3 are valuable in the lateralization in all epilepsies particularly in LFLE, NTLE and PCE. Lateralized E4-E6 and E7-10 are very useful in MTLE.
Electroencephalogram (EEG) is the primary tool in investigation of the brain science. It is necessary to carry out a deepgoing study into the characteristics and information hidden in EEGs to meet the needs of the clinical research. In this paper, we present a wavelet-nonlinear dynamic methodology for analysis of nonlinear characteristic of EEGs and delta, theta, alpha, and beta sub-bands. We therefore studied the effectiveness of correlation dimension (CD), largest Lyapunov exponen, and approximate entropy (ApEn) in differentiation between the interictal EEG and ictal EEG based on statistical significance of the differences. The results showed that the nonlinear dynamic characteristic of EEG and EEG subbands could be used as effective identification statistics in detecting seizures.
ObjectiveTo explore the clinical and video EEG features of patients with post-stroke epilepsy (PSE).MethodsThe clinical data of 68 patients with epilepsy after cerebral infarction and 33 patients with epilepsy after cerebral hemorrhage were analyzed retrospectively from January 2015 to June 2018 in the Affilated Hospital of Jining Medical University. There were 5 cases of early-onset epilepsy, and the rest were late-onset epilepsy. There were 68 cases of cerebral infarction (1 case showed post-infarction hemorrhagic transformation), 33 cases of cerebral hemorrhage; 51 females, 50 males (f∶m = 1.02∶1); the onset age was 45 ~ 101 years, with an average of (68.10 ± 10.26) years.ResultsThe time from seizure to stroke in 101 cases was (28.92 ± 35.61) months, 60 cases (59.40%) ≤ 1 year, 26 cases (25.74%) 1 ~ 5 years, and 15 cases (14.85%) 5 ~ 10 years. Post-stroke epilepsy had no relation to gender (P>0.05). The age of onset is mostly in 60 to 75 years old (62.38%). Seizure often happen within 1 year after stroke (59.4%). The type of attack is focal seizure (77.23%). Cortical infarction (77.94%), cerebral artery stenosis (83.82%), hypertension, diabetes, and atrial fibrillation are risk factors for epilepsy after infarction. The abnormal rate of EEG for PSE is 90.1%, which was manifested as slow wave in the lesion side, epileptic wave in the lesion side or contralateral side.ConclusionsThe location, duration, age and severity of cerebral artery stenosis in patients with PSE are closely related to the occurrence of seizure. VEEG plays an important role in the diagnosis, treatment and prognosis of epilepsy.