Electroencephalogram findings in 10 patients with post-stroke epilepsy:A retrospective study

BACKGROUND Post-stroke epilepsy is a common and easily overlooked complication of acute cerebrovascular disease.Long-term seizures can seriously affect the prognosis and quality of life of patients.Electroencephalogram(EEG)is the simplest way to diagnose epilepsy,and plays an important role in predicting seizures and guiding medication.AIM To explore the EEG characteristics of patients with post-stroke epilepsy and improve the detection rate of inter-seizure epileptiform discharges.METHODS From January 2017 to June 2020,10 patients with post-stroke epilepsy in our hospital were included.The clinical,imaging,and EEG characteristics were collected.The stroke location,seizure type,and ictal and interictal EEG manifestations of the patients with post-stroke epilepsy were then retrospectively analyzed.RESULTS In all 10 patients,epileptiform waves occurred in the side opposite to the stroke lesion during the interictal stage;these manifested as sharp wave,sharp-wave complex,or spike discharges in the anterior head lead of the side opposite to the lesion.CONCLUSION In EEG,epileptiform waves can occur in the side opposite to the stroke lesion in patients with post-stroke epilepsy.

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

Functional role of frontal electroencephalogram alpha asymmetry in the resting state in patients with depression:A review

Depression is a psychological disorder that affects the general public worldwide.It is particularly important to make an objective and accurate diagnosis of depression,and the measurement methods of brain activity have gradually received increasing attention.Resting electroencephalogram(EEG)alpha asymmetry in patients with depression shows changes in activation of the alpha frequency band of the left and right frontal cortices.In this paper,we review the findings of the relationship between frontal EEG alpha asymmetry in the resting state and depression.Based on worldwide studies,we found the following:(1)Compared with individuals without depression,those with depression showed greater right frontal EEG alpha asymmetry in the resting state.However,the pattern of frontal EEG alpha asymmetry in the resting state in depressive individuals seemed to disappear with age;(2)Compared with individuals without maternal depression,those with maternal depression showed greater right frontal EEG alpha asymmetry in the resting state,which indicated that genetic or experience-based influences have an impact on frontal EEG alpha asymmetry at rest;and(3)Frontal EEG alpha asymmetry in the resting state was stable,and little or no change occurred after antidepressant treatment.Finally,we concluded that the contrasting results may be due to differences in methodology,clinical characteristics,and participant characteristics.

Serum neuronal pentraxin 2 is related to cognitive dysfunction and electroencephalogram slow wave/fast wave frequency ratio in epilepsy

BACKGROUND Cognitive dysfunction in epileptic patients is a high-incidence complication.Its mechanism is related to nervous system damage during seizures,but there is no effective diagnostic biomarker.Neuronal pentraxin 2(NPTX2)is thought to play a vital role in neurotransmission and the maintenance of synaptic plasticity.This study explored how serum NPTX2 and electroencephalogram(EEG)slow wave/fast wave frequency ratio relate to cognitive dysfunction in patients with epilepsy.AIM To determine if serum NPTX2 could serve as a potential biomarker for diagnosing cognitive impairment in epilepsy patients.METHODS The participants of this study,conducted from January 2020 to December 2021,comprised 74 epilepsy patients with normal cognitive function(normal group),37 epilepsy patients with cognitive dysfunction[epilepsy patients with cognitive dysfunction(ECD)group]and 30 healthy people(control group).The minimental state examination(MMSE)scale was used to evaluate cognitive function.We determined serum NPTX2 levels using an enzyme-linked immunosorbent kit and calculated the signal value of EEG regions according to the EEG recording.Pearson correlation coefficient was used to analyze the correlation between serum NPTX2 and the MMSE score.RESULTS The serum NPTX2 level in the control group,normal group and ECD group were 240.00±35.06 pg/mL,235.80±38.01 pg/mL and 193.80±42.72 pg/mL,respectively.The MMSE score was lowest in the ECD group among the three,while no significant difference was observed between the control and normal groups.In epilepsy patients with cognitive dysfunction,NPTX2 level had a positive correlation with the MMSE score(r=0.367,P=0.0253)and a negative correlation with epilepsy duration(r=−0.443,P=0.0061)and the EEG slow wave/fast wave frequency ratio value in the temporal region(r=−0.339,P=0.039).CONCLUSION Serum NPTX2 was found to be related to cognitive dysfunction and the EEG slow wave/fast wave frequency ratio in patients with epilepsy.It is thus a potential biomarker for the diagnosis of cognitive impairment in patients with epilepsy.

Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy

Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles.This is a new strategy of human-computer interaction.A method of electroencephalogram(EEG) phase synchronization combined with band energy was proposed to construct a feature vector for pattern recognition of brain-computer interaction based on EEG induced by motor imagery in this paper,rhythm and beta rhythm were first extracted from EEG by band pass filter and then the frequency band energy was calculated by the sliding time window;the instantaneous phase values were obtained using Hilbert transform and then the phase synchronization feature was calculated by the phase locking value(PLV) and the best time interval for extracting the phase synchronization feature was searched by the distribution of the PLV value in the time domain.Finally,discrimination of motor imagery patterns was performed by the support vector machine(SVM).The results showed that the phase synchronization feature more effective in4s-7s and the correct classification rate was 91.4%.Compared with the results achieved by a single EEG feature related to motor imagery,the correct classification rate was improved by 3.5 and4.3 percentage points by combining phase synchronization with band energy.These indicate that the proposed method is effective and it is expected that the study provides a way to improve the performance of the online real-time brain-computer interaction control system based on EEG related to motor imagery.

Investigating the influence of monosodium L-glutamate on brain responses via scalp-electroencephalogram(scalp-EEG)

As the relevance between left and right brain neurons when transmitting electrical signals of umami taste is unknown,the aim of this work was to investigate responsive regions of the brain to the umami tastant monosodium glutamate(MSG)by using scalp-electroencephalogram(EEG)to identify the most responsive brain regions to MSG.Three concentrations of MSG(0.05,0.12,0.26 g/100 mL)were provided to participants for tasting while recoding their responsive reaction times and brain activities.The results indicated that the most responsive frequency to MSG was at 2 Hz,while the most responsive brain regions were T4 CzA2,F8 CzA2,and Fp2 CzA2.Moreover,the sensitivity of the brain to MSG was significantly higher in the right brain region.This study shows the potential of using EEG to investigate the relevance between different brains response to umami taste,which contributes to better understanding the mechanism of umami perception.

Effect of the environment in Antarctica on immune function and electroencephalogram

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THE EFFECT OF ACUPUNCTURING ACUPOINTS ON THE CHANGE OF ELECTROENCEPHALOGRAM (EEG) IN ENDOTOXIC SHOCKED RATS

In present work,EEG and BP were used as the indexes to observe the relationbetween the change of EEG and the change of BP in the endotoxic shocked rats。At maintainingshock for 1 hr,dysrhythmia of EEG appeared in 38/46 cases.Simultaneously,there was a markeddrop in Bp,P【0.05.Following the shocked time prolonged,dysrhythmia was getting severe。AfterEA”Rengzhong"(n=14)or“Zusanli”(n=12),BP was significantly increased(P【0.05),anddysrhythmia of EEG showed clear improvement in most of the rats。There was a close relation be-tween the changes of EEG and BP,the change of EEG had a direct bearing on the change of BP.

Electroencephalogram characters of parallax stereo watching asthenopia

The electroencephalogram(EEG)characters value of observers can be deduced by collecting brain electrical information when the observers are watching parallax stereo video.The characters value will change clearly when watching asthenopia appear.To investigate the characters of parallax stereo watching asthenopia,the EEG of observers were recorded through the whole watching process of parallax stereo films until watching asthenopia appeared.The recorded EEG data of observers belongs to time-domain information.Fourier transform can process these data to frequency spectrum information.Theαandβwaves average power can be got by Newton-Cotes equation from the information.The ratio ofβpower to the sum ofαandβpower,CV,can be defined as EEG characters value of parallax stereo watching asthenopia and used to estimate the asthenopia degree of observers.Our experiments show that the smaller the CVis,the more serious the asthenopia is.

Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning

The detection of alcoholism is of great importance due to its effects on individuals and society.Automatic alcoholism detection system(AADS)based on electroencephalogram(EEG)signals is effective,but the design of a robust AADS is a challenging problem.AADS’current designs are based on conventional,hand-engineered methods and restricted performance.Driven by the excellent deep learning(DL)success in many recognition tasks,we implement an AAD system based on EEG signals using DL.A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD problem.In order to solve this problem,we propose a multi-channel Pyramidal neural convolutional(MP-CNN)network that requires a less number of learnable parameters.Using the deep CNN model,we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or normal.We validate the robustness and effectiveness of proposed AADS using KDD,a benchmark dataset for alcoholism detection problem.In order to find the brain region that contributes significant role in AAD,we investigated the effects of selected 19 EEG channels(SC-19),those from the whole brain(ALL-61),and 05 brain regions,i.e.,TEMP,OCCIP,CENT,FRONT,and PERI.The results show that SC-19 contributes significant role in AAD with the accuracy of 100%.The comparison reveals that the state-of-the-art systems are outperformed by the AADS.The proposed AADS will be useful in medical diagnosis research and health care systems.

Clinical and electroencephalogram characteristics and treatment outcomes in children with benign epilepsy and centrotemporal spikes

BACKGROUND Epilepsy is a syndrome characterized by transient,rigid,paroxysmal,and repetitive central nervous system dysfunction.Prevention,control,and improvement of cognitive and behavioral dysfunction are of great significance for improving the patients’intellectual development and quality of life.Electroencephalograms(EEG)can predict an accelerated decline in cognitive function.AIM To determine the clinical and EEG characteristics and treatment results of benign epilepsy in spiking children.METHODS A total of 106 cases of benign epilepsy in children with myocardial spines treated at our hospital from January 2017 to January 2020 were selected.Differences in clinical data and EGG characteristics between treatment-effective/-ineffective patients were analyzed,and children’s intellectual development before and after treatment evaluated using the Gesell Development Diagnostic Scale.RESULTS EEG showed that the discharge proportion in the awake and sleep periods was 66.04%,and the peak/peak discharge was mainly single-sided,accounting for 81.13%,while the discharge generalization accounted for 31.13%.There was no significant difference in any of these variables between sexes and ages(P>0.05).The proportion of patients with early onset(<5 years old)and seizure frequency>3 times/half a year was 40.00%and 60.00%,respectively;the incidence rate and seizure frequency in the younger age group(<5 years old)were significantly higher than those in the treatment-effective group(P<0.05),while the discharge index was significantly lower than that in the treatment-effective group(P<0.05).The discharge index was negatively correlated with fine motor skill and language development(r=-0.274 and-0.247,respectively;P<0.05),but not with the rest(P>0.05).Logistic regression analysis showed that low age onset(<5 years old)and seizure frequency were the factors affecting ineffective-treatment of benign epilepsy in children(odds ratio=11.304 and 5.784,respectively;P<0.05).The discharge index of the responsive group after treatment was significantly lower than that of the unresponsive group(P<0.05).However,there was no significant difference between groups after treatment in gross and fine motor skills,adaptability,language,and personal social development(P>0.05).CONCLUSION The EEG of children with benign epilepsy due to spinal wave in central time zone has characteristic changes,and the therapeutic effect is influenced by age of onset and attack frequency.

ENERGY FEATURE EXTRACTION AND SVM CLASSIFICATION OFMOTORIMAGERY-INDUCED ELECTROENCEPHALOGRAMS

The precise classification for the electroencephalogram(EEG)in different mental tasks in the research on brain
computer interface(BCI)is the key for the design and clinical application of the system.In this paper,a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments.First,to eliminate the low frequency noise in the original EEGs,the signals were decomposed by empirical mode decomposition(EMD)and then the optimal kernel parameters for support vector machine(SVM)were determined,the energy features of thefirst three intrinsic mode functions(IMFs)of every signal were extracted and used as input vectors of the employed SVM.The output of the SVM will be classification result for different mental task EEG signals.The study shows that mean identification rate of the proposed algorithm is 95%,which is much better than the present traditional algorithms.

Workers’Cerebrocortical Activity in Hot and Humid Condition:An Electroencephalogram Study

Hyperthermal environments can harm workers’health and safety.However,it is difficult to include effective protection into standards because heat-related impacts vary significantly accord-ing to individual workers and multiple factors.Studies suggested obvious relationship between en-vironment condition and bio-electricity signal,including electroencephalogram(EEG)signal.We used a detector with 64 electrodes to perform dedicated EEG measurements of nine individual sub-jects to analyze human cerebral activity under hyperthermal(35℃,80%RH)and standard condi-tions(25℃,30%RH).Amplitude changes of the frequency wavebands were analyzed using statist-ical analysis.Seven participants showed increasing beta activity due to high temperature and high humidity in the primary somatosensory cortex(electrode C3)and the temporopolar region(elec-trode FT 8).The amplitude value of alpha wave is increased from 0.194 to 0.213 while the amp-litude value of beta wave is increased from 0.144 to 0.160.Value is decreased due to hyperthermal environment for most people.The results of this study could be used to inform the development of wearable equipment to monitor the health of on-site workers,which is fundamental to improve worker safety and wellbeing.

Classification of Epileptic Electroencephalograms Using Time-Frequency and Back Propagation Methods

Today,electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor.These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain,such as epilepsy.Electroencephalogram(EEG)signals are however prone to artefacts.These artefacts must be removed to obtain accurate and meaningful signals.Currently,computer-aided systems have been used for this purpose.These systems provide high computing power,problem-specific development,and other advantages.In this study,a new clinical decision support system was developed for individuals to detect epileptic seizures using EEG signals.Comprehensive classification results were obtained for the extracted filtered features from the time-frequency domain.The classification accuracies of the time-frequency features obtained from discrete continuous transform(DCT),fractional Fourier transform(FrFT),and Hilbert transform(HT)are compared.Artificial neural networks(ANN)were applied,and back propagation(BP)was used as a learning method.Many studies in the literature describe a single BP algorithm.In contrast,we looked at several BP algorithms including gradient descent with momentum(GDM),scaled conjugate gradient(SCG),and gradient descent with adaptive learning rate(GDA).The most successful algorithm was tested using simulations made on three separate datasets(DCT_EEG,FrFT_EEG,and HT_EEG)that make up the input data.The HT algorithm was the most successful EEG feature extractor in terms of classification accuracy rates in each EEG dataset and had the highest referred accuracy rates of the algorithms.As a result,HT_EEG gives the highest accuracy for all algorithms,and the highest accuracy of 87.38%was produced by the SCG algorithm.

The complexity of occupational stress electroencephalogram

It is an important method for using electroencephalogram (EEG) to detect and diagnose occupational Stress in clinical practice. In this paper, the complexity analysis method based on Jensen-Shannon Divergence was used to calculate the complexity of occupational stress electroencephalogram from students and nurses.The study found that the complexity of nurses’ EEG was higher than that of students’ EEG. The result can be used to assisted clinical diagnosis.

Ambulatory Electroencephalograms in Neuropsychiatric Practice: Opening Pandora’s Jar

Since the advent of imaging studies such as magnetic resonance imaging (MRI), the role of electroencephalograms (EEGs) has diminished. Simultaneously, computerized scanning and miniaturization of the EEG and its components have allowed us to obtain lengthier recordings in an ambulatory setting. We report on 261 ambulatory electroencephalograms performed consecutively in the two year period of 2011 and 2012 in a busy neurology and neuropsychiatry practice with predominantly geriatric patient population. 23% of these patients had abnormal AEEGs demonstrating clear-cut epileptogenic discharges. The role of these findings in clinical practice, especially in geriatric and psychiatric populations is discussed.

Erratum to “Ambulatory Electroencephalograms in Neuropsychiatric Practice: Opening Pandora’s Jar”

Since the advent of imaging studies such as magnetic resonance imaging (MRI), the role of electroencephalograms (EEGs) has diminished. Simultaneously, computerized scanning and miniaturization of the EEG and its components have allowed us to obtain lengthier recordings in an ambulatory setting. We report on 261 ambulatory electroencephalograms performed consecutively in the two year period of 2011 and 2012 in a busy neurology and neuropsychiatry practice with predominantly geriatric patient population. 23% of these patients had abnormal AEEGs demonstrating clear-cut epileptogenic discharges. The role of these findings in clinical practice, especially in geriatric and psychiatric populations is discussed.

Correlation between level of serum prolactin and epileptic discharges of electroencephalogram from 24 to 36 hours after epileptic onset

BACKGROUND: Researchers discovered that serum prolactin could rise following an epileptic seizure. The prolactin level might reach three times more than basic level within 30 minutes and decrease to the normal value 2 hours after the seizure occurred. The mechanism might result in an increase of serum prolactin concentrations with the activation of the hypothalamic-pituitary axis. OBJECTIVE:To probe into the correlation between changes of serum prolactin and incidence of epileptic discharges of electroencephalogram (EEG) at 24-36 hours after epileptic onset of patients with secondary epilepsy. DESIGN: Clinical observational study. SETTING: Department of Neurology, First Hospital affiliated to Soochow University. PARTICIPANTS: A total of 21 patients with secondary epilepsy were selected from the Department of Neurological Emergency or Hospital Room of the First Hospital affiliated to Soochow University from November 2005 to April 2006. There were 14 males and 7 females aged from 25 to 72 years. All patients met International League Anti-epileptic (ILAE) criteria in 1981 for secondary generalized tonic clonic seizure through CT or MRI and previous EEG. All patients were consent. Primary diseases included cerebral trauma (3 cases), tumor (2 cases), stroke (7 cases) and intracranial infeion (9 cases). METHODS: Venous blood of all patients was collected at 24-36 hours after epileptic onset. Serum prolactin kit (Beckman Coulter, Inc in USA) was used to measure value of serum prolactin according to kit instruction. Then, value of serum prolactin was compared with the normal value (male: 2.64-13.13 mg/L; female: 3.34-26.72 mg/L); meanwhile, EEG equipment (American Nicolet Incorporation) was used in this study. MAIN OUTCOME MEASURES: ① Abnormal rate of serum prolactin of patients with secondary epilepsy; ②Comparison between normal and abnormal level of serum prolactin and incidence of EEG epileptic discharge of patients with secondary epilepsy. RESULTS:All 21 patients with secondary epilepsy were involved in the final analysis. ① Results of serum prolactin level: Among 21 patients with of secondary epilepsy, 10 of them had normal serum prolactin and 11 had abnormal one, and the abnormal rate was 52% (11/21). ② Detecting results of EEG: EEG results showed that 6 cases were normal and 15 were abnormal, and the abnormal rate was 71% (15/21). The symptoms were sharp wave, spike wave or sharp slow wave, spike slow wave of epileptic discharges in 8 cases, which was accounted for 38%. ③ Correlation between abnormality of serum prolactin and EEG epileptic wave: Eleven cases had abnormal serum prolactin, and the incidence was 64% (7/11), which was higher of epileptic wave than that of non-epileptic wave [36% (4/11), P < 0.05]; however, 10 cases had normal serum prolactin, and the incidence was 10% (1/10). Epileptic wave was lower than non-epileptic wave [90% (9/10), P < 0.01]. CONCLUSION: The level of serum prolactin of patients with secondary epilepsy is abnormally increased at 24-36 hours after epileptic onset; in addition, incidence of epileptic discharge is also increased remarkably.

Revealing True and False Brain States Based on Wavelet Analysis of Electroencephalogram

Statement of the Problem: As you know, there exist two different states in the brain’s mental activity: true and false. In recent years, a progressive method of wavelet transformation of the electroencephalogram (EEG) has been developed, which enabled us to establish the fundamental possibility of direct objective registration of the human brain’s mental activity. Earlier, we created an experimental model and software for recognizing true and false mental responses of a person based on the EEG wavelet transformation and described it in the article. The developed experimental model and information software made it possible to compare the two mental states of brain activity by electroencephalographic indicators, one of which is false and the other is true. The goal is to develop a fundamentally new information technology for recognizing true and false states in the brain’s mental activity based on the wavelet transformation of the electroencephalogram. Results: It was revealed that the true and false states of the brain can be distinguished using the method of continuous wavelet transformation and calculation of the EEG wavelet energy. It is shown that the main differences between true and false mental responses are observed in the delta and alpha ranges of the EEG. In the EEG delta rhythm, the wavelet energy is reliably higher in case of a false answer compared to a true one. In the EEG alpha rhythm, the wavelet energy is significantly higher with a true answer than a false one. Practical significance of the research: The data obtained open up the fundamental possibility of identifying true and false mental states of the brain on the basis of continuous wavelet transformation and calculation of the EEG wavelet energy.

Correlation analysis of Toll-like receptor 3 content in peripheral blood with electroencephalogram parameters and neurotransmitter content in children with intractable epilepsy

Objective:To detect the content of Toll-like receptor 3 (TLR3) in peripheral blood of children with intractable epilepsy, explore the correlation between TLR3 content and EEG parameters, neurotransmitter contents.Methods:37 cases of Intractable epilepsy children in our hospital during September 2016to June 2018 were chosen as Intractable epilepsy group, 30 cases of healthy volunteers who underwent physical examination in our hospital were treated as Normal control group. The levels of TLR3, neurotransmitter [5-hydroxytryptamine (5-HT), dopamine (DA), epinephrine (E), norepinephrine (NE)] and electroencephalogram parameters [alpha, beta, delta, theta] in peripheral blood of two groups were compared. Pearson test was used to evaluate the correlation of TLR3 content in peripheral blood with EEG parameters and neurotransmitter content in children with intractable epilepsy.Results: Content of TLR3 in peripheral blood of Intractable epilepsy group was significantly higher than that of Normal control group;the alpha power and theta power of EEG parameters were lower than those of Normal control group;contents of neurotransmitters such as 5-HT, DA, E and NE were significantly lower than those of Normal control group (P<0.05). The correlation analysis showed that content of TLR3 in peripheral blood of children with intractable epilepsy was negatively correlated with levels of alpha and theta power of EEG, positively correlated with content of neurotransmitters such as 5-HT, DA, E and NE (P<0.05), but had no significant correlation was found with level of beta and delta power (P>0.05).Conclusion: The abnormal increase of TLR3 in peripheral blood of children with intractable epilepsy may be one of the direct causes of neurological impairment in children with intractable epilepsy.