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1.
Rev. cuba. med ; 61(2): e2871, abr.-jun. 2022. tab
Article in Spanish | LILACS, CUMED | ID: biblio-1408995

ABSTRACT

Introducción: El monitoreo continuo del Electroencefalograma, es la recogida simultánea de la actividad cerebral y la conducta clínica por un período de horas a días. Por el alto costo de la técnica aún no está muy difundida. Objetivos: Evaluar la utilidad del monitoreo electroencefalográfico continuo en el paciente crítico. Métodos: Se realizó un estudio descriptivo, retrospectivo y longitudinal en 118 sujetos mayores de 19 años ingresados en las unidades de terapia del Hospital Clínico Quirúrgico Hermanos Ameijeiras; entre noviembre 2016 a octubre 2018 con indicación de un Electroencefalograma continuo. Se consideraron variables clínicas y electroencefalográficas: escala de Glasgow, ocurrencia de crisis, diagnóstico, estado al egreso, anormalidad del Electroencefalograma, descargas epileptiformes, sospecha de estatus epiléptico no convulsivo por electroencefalograma entre otras. Los datos se procesaron con test de Chi cuadrado, test de Mc Nemar y test t de student, se empleó un nivel de significación de p≤0.05. Resultados: 60 de los pacientes pertenecían al sexo femenino, la mediana de las edades fue 67,5 años. La escala de Glasgow mostró asociación significativa con el grado de anormalidad del electroencefalograma (p=0,001), es la arreactividad y la discontinuidad de la actividad de base predictores de pobre pronóstico. Se observaron descargas epileptiformes periódicas en 100 pacientes. Se definió estatus epiléptico no convulsivo en 56 sujetos (37,28 por ciento) y en 81 sujetos (68,64 por ciento) el resultado del electroencefalograma motivó una conducta médica. Conclusiones: El monitoreo continuo del electroencefalograma es útil en el diagnóstico y manejo del paciente con episodios no convulsivos, permite formular un pronóstico neurológico y orientó la conducta médica(AU)


Introduction: The continuous monitoring of the electroencephalogram is the simultaneous collection of brain activity and clinical behavior for a period of hours to days. Due to the high cost of the technique, it is not yet widely used. Objectives: To evaluate the usefulness of continuous electroencephalographic monitoring in critically ill patients. Methods: A descriptive, retrospective and longitudinal study was carried out in 118 subjects over 19 years of age admitted to the therapy units at Hermanos Ameijeiras Surgical Clinical Hospital; from November 2016 to October 2018. They were indicated a continuous electroencephalogram. Clinical and electroencephalographic variables were considered, such as Glasgow scale, seizure occurrence, diagnosis, discharge status, electroencephalogram abnormality, epileptiform discharges, suspicion of nonconvulsive status epilepticus by electroencephalogram, among others. The data was processed with the Chi square test, the Mc Nemar test and the student's t test, using significance level of p≤0.05. Results: Sixty patients were female, the median age was 67.5 years. The Glasgow scale showed significant association with the degree of electroencephalogram abnormality (p=0.001). A reactivity and discontinuity of baseline activity are predictors of poor prognosis. Periodic epileptiform discharges were observed in 100 patients. Non-convulsive status epilepticus was defined in 56 subjects (37.28 percent) and in 81 subjects (68.64 percent) the result of the electroencephalogram motivated a medical procedure. Conclusions: The continuous monitoring of the electroencephalogram is useful in the diagnosis and management of patients with non-convulsive episodes, it allows formulating a neurological prognosis and guided medical conduct(AU)


Subject(s)
Humans , Male , Female , Critical Illness , Electroencephalography/methods , Epidemiology, Descriptive , Retrospective Studies , Longitudinal Studies
2.
Arq. neuropsiquiatr ; 80(1): 43-47, Jan. 2022. graf
Article in English | LILACS | ID: biblio-1360137

ABSTRACT

ABSTRACT Background: In light of the established challenges of resident EEG education worldwide, we sought to better understand the current state of neurology resident EEG education in Brazil. Objective: To define Brazilian EEG practices including in-residency requirements for EEG training and competency. Methods: We assessed the perspectives of adult residents (PGY1-3) on EEG education and their level of confidence interpreting EEG with a 24-question online survey. Results: We analyzed 102 responses from 52 Brazilian neurology residency programs distributed in 14 states. There were 18 PGY1s, 45 PGY2s, and 39 PGY3s. Ninety-six percent of participants reported that learning how to read EEG during residency was very or extremely important. The most commonly reported barriers to EEG education were insufficient EEG exposure (70%) and ineffective didactics (46%). Residents believed that standard EEG lectures were the most efficient EEG teaching method followed by interpreting EEG with attendings' supervision. Roughly half of residents (45%) reported not being able to read EEG even with supervision, and approximately 70% of all participants did not feel confident writing an EEG report independently. Conclusion: Despite the well-established residency EEG education requirements recommended by the Brazilian Academy of Neurology (ABN), there seems to be a significant lack of comfort interpreting EEG among Brazilian adult neurology residents. We encourage Brazilian neurology residency leadership to re-evaluate the current EEG education system in order to ensure that residency programs are following EEG education requirements and to assess whether EEG benchmarks require modifications.


RESUMO Antecedentes: Diante dos desafios da educação em EEG estabelecidos em todo o mundo, buscamos compreender melhor o estado atual da educação em EEG durante a residência de neurologia no Brasil. Objetivo: Investigar práticas de EEG no Brasil, incluindo requisitos para treinamento e competência durante a residência de neurologia. Métodos: Avaliamos as perspectivas dos residentes (R1-3) de neurologia (adulto) sobre educação em EEG e nível de confiança ao interpretá-lo através de questionário online de 24 perguntas. Resultados: Foram analisadas 102 respostas de 52 programas de residência distribuídos em 14 estados. Dezoito R1s, 45 R2s e 39 R3s responderam à pesquisa. Noventa e seis por cento dos participantes relataram que aprender a ler EEG durante a residência é muito ou extremamente importante. As barreiras mais relatadas para educação em EEG foram exposição insuficiente ao EEG (70%) e didática ineficaz (46%). Os participantes apontaram aulas como método de ensino mais eficaz, seguido pela interpretação do EEG supervisionada pelos chefes. Aproximadamente metade dos residentes (45%) relatou não ser capaz de ler EEG mesmo com supervisão e cerca de 70% não se sente confiante para escrever um laudo de EEG de forma independente. Conclusões: Apesar dos requisitos estabelecidos pela Academia Brasileira de Neurologia (ABN) sobre ensino de EEG durante a residência, há significativa falta de confiança na sua interpretação pelos residentes de neurologia (adulto). Incentivamos as lideranças a reavaliar o sistema de educação para garantir que os programas de residência sigam requisitos de educação em EEG e se os benchmarks de EEG requerem modificações.


Subject(s)
Humans , Adult , Internship and Residency , Neurology , United States , Brazil , Surveys and Questionnaires , Educational Status , Electroencephalography/methods
3.
Ethiopian Journal of Health Sciences ; 32(5): 905-912, 5 September 2022. Figures, Tables
Article in English | AIM | ID: biblio-1398219

ABSTRACT

Little is known about the characteristics of electroencephalogram (EEG) findings in epileptic patients in Ethiopia. The objective of this study was to characterize the EEG patterns, indications, antiepileptic drugs (AEDs), and epilepsy risk factors. METHODS: A retrospective observational review of EEG test records of 433 patients referred to our electrophysiology unit between July 01, 2020, and December 31, 2021. Results: The age distribution in the study participants was right skewed unipolar age distribution for both sexes and the mean age of 33.8 (SD=15.7) years. Male accounted for 51.7%. Generalized tonic clonic seizure was the most common seizure type. The commonest indication for EEG was abnormal body movement with loss of consciousness (35.2%). Abnormal EEG findings were observed in 55.2%; more than half of them were Interictal epileptiform discharges, followed by focal/or generalized slowing. Phenobarbitone was the commonest AEDs. A quarter (20.1%) of the patients were getting a combination of two AEDs and 5.2% were on 3 different AEDs. Individuals taking the older AEDs and those on 2 or more AEDs tended to have abnormal EEG findings. A cerebrovascular disorder (27.4%) is the prevalent risk factor identified followed by brain tumor, HIV infection, and traumatic head injury respectively. CONCLUSION: High burden of abnormal EEG findings among epileptic patients referred to our unit. The proportion of abnormal EEG patterns was higher in patients taking older generation AEDs and in those on 2 or more AEDs. Stroke, brain tumor, HIV infection and traumatic head injury were the commonest identified epilepsy risk factors


Subject(s)
Patient Discharge , Trigeminal Neuralgia , Electroencephalography , Epilepsy , Risk Factors , Ethiopia
4.
Article in Chinese | WPRIM | ID: wpr-928898

ABSTRACT

To solve the problem of real-time detection and removal of EEG signal noise in anesthesia depth monitoring, we proposed an adaptive EEG signal noise detection and removal method. This method uses discrete wavelet transform to extract the low-frequency energy and high-frequency energy of a segment of EEG signals, and sets two sets of thresholds for the low-frequency band and high-frequency band of the EEG signal. These two sets of thresholds can be updated adaptively according to the energy situation of the most recent EEG signal. Finally, we judge the level of signal interference according to the range of low-frequency energy and high-frequency energy, and perform corresponding denoising processing. The results show that the method can more accurately detect and remove the noise interference in the EEG signal, and improve the stability of the calculated characteristic parameters.


Subject(s)
Algorithms , Electroencephalography , Signal Processing, Computer-Assisted , Signal-To-Noise Ratio , Wavelet Analysis
5.
Article in Chinese | WPRIM | ID: wpr-928849

ABSTRACT

OBJECTIVE@#Exploring the effectiveness of using EEG linear and nonlinear features for accessing mental workload in different tasks.@*METHODS@#Working memory tasks with different information types and various mental loads were designed based on N-Back paradigm. EEG signals from 18 normal adults were acquired when tasks were being performed. Linear and nonlinear features of EEGs were then extracted. Indices that can effectively reflect mental workload levels were selected by using multivariate analysis of variance statistical approach.@*RESULTS@#With the increment of task load, power of frontal Theta, Theta/Alpha ratio, and sample entropies (scales>10) in parietal regions increased significantly first and decreased slightly then, while the power of central-parietal Alpha decreased significantly first and increased slightly then. No difference in power of frontal Theta, central-parietal Alpha, and sample entropies (scales>10) of parietal regions were found between verbal and object tasks, as well as between two spatial tasks. No difference of frontal Theta/Alpha ratio was found in all the four tasks.@*CONCLUSIONS@#The results can provide evidence for the mental workload evaluation in tasks with different information types.


Subject(s)
Electroencephalography , Memory, Short-Term , Workload
6.
Article in Chinese | WPRIM | ID: wpr-939756

ABSTRACT

This study introduces a portable multi-channel EEG signal acquisition system. The system is mainly composed of EEG electrode connector, signal conditioning circuit, EEG acquisition part, main control MCU and power supply part. The low-power EEG acquisition front-end ADS1299 and STM32 are used to form the signal acquisition and data communication part. The collected EEG signal can be transmitted to the PC for real-time display. After relevant tests, the system has small volume, low power consumption, high signal-to-noise ratio, and meets the requirements of portable wearable medical devices.


Subject(s)
Electric Power Supplies , Electrodes , Electroencephalography , Signal Processing, Computer-Assisted , Signal-To-Noise Ratio
7.
Article in Chinese | WPRIM | ID: wpr-939617

ABSTRACT

Transcranial direct current stimulation (tDCS) has become a new method of post-stroke rehabilitation treatment and is gradually accepted by people. However, the neurophysiological mechanism of tDCS in the treatment of stroke still needs further study. In this study, we recruited 30 stroke patients with damage to the left side of the brain and randomly divided them into a real tDCS group (15 cases) and a sham tDCS group (15 cases). The resting EEG signals of the two groups of subjects before and after stimulation were collected, then the difference of power spectral density was analyzed and compared in the band of delta, theta, alpha and beta, and the delta/alpha power ratio (DAR) was calculated. The results showed that after real tDCS, delta band energy decreased significantly in the left temporal lobes, and the difference was statistically significant ( P < 0.05); alpha band energy enhanced significantly in the occipital lobes, and the difference was statistically significant ( P < 0.05); the difference of theta and beta band energy was not statistically significant in the whole brain region ( P > 0.05). Furthermore, the difference of delta, theta, alpha and beta band energy was not statistically significant after sham tDCS ( P > 0.05). On the other hand, the DAR value of stroke patients decreased significantly after real tDCS, and the difference was statistically significant ( P < 0.05), and there was no significant difference in sham tDCS ( P > 0.05). This study reveals to a certain extent the neurophysiological mechanism of tDCS in the treatment of stroke.


Subject(s)
Brain/physiopathology , Brain Waves/physiology , Electroencephalography/methods , Humans , Stroke/therapy , Stroke Rehabilitation/methods , Transcranial Direct Current Stimulation/methods
8.
Article in Chinese | WPRIM | ID: wpr-939616

ABSTRACT

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.


Subject(s)
Algorithms , Brain-Computer Interfaces , Electroencephalography/methods , Humans , Imagery, Psychotherapy , Imagination
9.
Chinese Journal of Pediatrics ; (12): 345-349, 2022.
Article in Chinese | WPRIM | ID: wpr-935699

ABSTRACT

Objective: To summarize the phenotypes of epilepsy in patients with MBD5 gene variants. Methods: A total of 9 epileptic patients, who were treated in the Department of Pediatrics, Peking University First Hospital from July 2016 to September 2021 and detected with MBD5 gene pathogenic variants, were enrolled. The features of clinical manifestations, electroencephalogram (EEG), and neuroimaging were analyzed retrospectively. Results: Among 9 patients, 6 were male and 3 were female. Age at seizure onset ranged from 5 to 89 months. Multiple seizure types were observed, including generalized tonic clonic seizures (GTCS) in 7 patients, myoclonic seizures in 5 patients, focal seizures in 5 patients, atypical absence seizures in 3 patients, atonic seizures in 2 patients, myoclonus absence seizures in 1 patient, epileptic spasms in 1 patient, and tonic seizures in 1 patient. There were 8 patients with multiple seizure types, 2 patients with sensitivity to fever and 5 patients with clustering of seizures. Two patients had a history of status epilepticus. All patients had developmental delay before seizure onset. Nine patients had obvious language delay, and 6 patients had autism-like manifestations. Five patients had slow background activity in EEG. Interictal EEG showed abnormal discharges in 9 patients. Brain magnetic resonance imaging (MRI) was normal in all patients. A total of 9 epileptic patients carried MBD5 gene variants, all of them were de novo variants. There were MBD5 gene overall heterozygous deletion in 1 patient, large fragment deletions including MBD5 gene in 3 patients and single nucleotide variations (c.300C>A/p.C100X, c.1775delA/p.N592Tfs*29, c.1759C>T/p.Q587X, c.150_151del/p.Lys51Asnfs*6, c.113+1G>C) in 5 patients. The age at last follow-up ranged from 2 years and 9 months to 11 years and 11 months. At the last follow-up, 2 patients were seizure-free for more than 11 months to 4 years 6 months, 7 patients still had seizures. Conclusions: The initial seizure onset in patients with MBD5 gene variants usually occurs in infancy. Most patients have multiple seizure types. The seizures may be fever sensitive and clustered. Developmental delays, language impairments, and autistic behaviors are common. MBD5 gene variants include single nucleotide variations and fragment deletions. Epilepsy associated with MBD5 gene variants is usually refractory.


Subject(s)
Child , Child, Preschool , DNA-Binding Proteins/genetics , Electroencephalography , Epilepsies, Myoclonic/genetics , Epilepsy/genetics , Female , Fever , Humans , Infant , Male , Nucleotides , Phenotype , Retrospective Studies , Seizures/genetics
10.
Chinese Journal of Pediatrics ; (12): 339-344, 2022.
Article in Chinese | WPRIM | ID: wpr-935698

ABSTRACT

Objective: To investigate the clinical and genetic characteristics of epilepsy associated with chromosome 16p11.2 microdeletion. Methods: The patients (n=10) with 16p11.2 microdeletion found in children with epilepsy treated in Beijing Children's Hospital Affiliated to Capital Medical University from January 2018 to January 2021 were collected. The clinical manifestations, gene variations and prognosis were analyzed retrospectively. Results: A total of 10 children's data were collected, including 5 male and 5 female. The onset age of epilepsy was 4.5 (4.1,5.0) months. Regarding the seizure types, 7 cases had focal seizures with secondary generalization, 2 cases had generalized seizures, and 1 case had tonic seizures and spasms. Nine cases had cluster seizure attacks and 3 cases had status epilepticus. Seven cases had focal or multifocal epileptiform discharges in interictal electroencephalogram (EEG), 3 cases had borderline or normal EEG. Brain magnetic resonance imaging showed polymicrogyria in 1 case, paraventricular leukomalacia in 1 case, delayed myelination of white matter in 3 cases, and no obvious abnormalities in the other 5 cases. The patients were followed up for 0.5-3.5 years, with 1-3 kinds of antiepileptic drugs taken orally. The case with polymicrogyria still had seizures, however the other 9 cases had seizures controlled. The age of the last seizure attack was 8 (6, 12) months. There were 6 cases with mental and motor developmental delay before epilepsy onset. During the follow-up, 7 cases were retarded to varying degrees, while 3 cases had normal development. Regarding the genetic detection methods, 7 cases underwent whole exome sequencing, 2 cases underwent whole genome copy number variation detection, and 1 case underwent whole genome sequencing. The length of the 16p11.2 deletion in 10 cases ranged from 525 to 951 kb, and all contained the PRRT2 gene intact. Six cases were de novo variants, 1 case was inherited from the mother who had a history of convulsions in early childhood, and the source of variant was not verified in 3 cases, none of whose parents had relevant phenotype. Conclusions: The epilepsy associated with 16p11.2 microdeletion is mainly induced by the heterozygous deletion of PRRT2 gene in this region, however the phenotype is usually severe, and often combined with developmental and epileptic encephalopathy. Detection of copy number variation should be emphasized in children whose etiology is considered genetic but second-generation sequencing result is negative.


Subject(s)
Child, Preschool , Chromosomes , DNA Copy Number Variations , Electroencephalography , Epilepsy/genetics , Female , Humans , Male , Polymicrogyria/genetics , Retrospective Studies , Seizures/genetics
11.
Chinese Journal of Pediatrics ; (12): 232-236, 2022.
Article in Chinese | WPRIM | ID: wpr-935676

ABSTRACT

Objective: To analyse the clinical and gene characteristics of GRIN2B gene related neurological developmental disorders in children. Methods: The data of 11 children with GRIN2B gene related neurological developmental disorders from November 2016 to February 2021 were collected from Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health and analyzed retrospectively. The clinical features, electroencephalogram (EEG), brain imaging and gene testing results were summarized. Results: Among 11 children 6 were boys and 5 were girls. Two of them were diagnosed with developmental and epileptic encephalopathy. The ages of seizures onset were 3 months and 9 months, respectively. Seizure types included epileptic spasm, tonic seizures, tonic spasm and focal seizures, and 1 patient also had startle attacks. EEG showed interictal multifocal epileptiform discharges. Both of them were added with more than 2 anti-seizure drugs, which were partially effective but could not control. They had moderate to severe mental and motor retardation. The phenotype of 9 cases was developmental delay or intellectual disability without epilepsy, age of visit 1 year to 6 year and 4 months of whom 5 cases had severe developmental delay, 2 cases had moderate and 2 cases had mild delay. Multi-focal epileptiform discharges were observed in 3 cases, no abnormality was found in 3 cases, and the remaining 3 cases did not undergo EEG examination. Ten cases underwent brain magnetic resonance imaging (MRI), 6 cases had nonspecific abnormalities and 4 cases were normal. Nine GRIN2B gene heterozygous variants were detected by next-generation sequencing in these 11 patients, 8 cases had missense variants and 1 case had nonsense variant, all of which were de novo and 3 of which were novel. Missense variants were found in 10 patients, among them 6 cases had severe developmental delay, 3 cases had moderate and 1 case had mild developmental delay, but the patient with nonsense variant showed mild developmental delay without epilepsy. Conclusions: The phenotypes of GRIN2B gene related neurological developmental disorders in children are diverse, ranging from mild intellectual impairment without epilepsy to severe epileptic encephalopathy. Patients with epileptic phenotype usually have an onset age of infancy, and spasm and focal seizures are the most common seizure types. And the epiletice episodes are refractory. Most of the patients with missense variants had severe developmental delay.


Subject(s)
Child , Developmental Disabilities/genetics , Electroencephalography , Epilepsy/genetics , Female , Humans , Infant , Male , Retrospective Studies , Seizures/genetics , Spasms, Infantile/genetics
12.
Chinese Journal of Pediatrics ; (12): 51-55, 2022.
Article in Chinese | WPRIM | ID: wpr-935639

ABSTRACT

Objective: To explore the clinical manifestations and genetic characteristics of patients with epilepsy and episodic ataxia caused by SCN2A gene variation. Methods: The clinical data of seizure manifestation, imaging examination and genetic results of 5 patients with epilepsy and (or) episodic ataxia because of SCN2A gene variation admitted to the Department of Pediatrics, the Third Affiliated Hospital of Zhengzhou University from July 2017 to January 2021 were analyzed retrospectively. Results: Among 5 patients, 4 were female and 1 was male. The onset age of epilepsy ranged from 4 days to 8 months. There were 2 cases of benign neonatal or infantile epilepsy and 3 cases of epileptic encephalopathy, in whom 1 case had development retardation,1 case transformed from West syndrome to infantile spasm and another one transformed from infantile spasm to Lennox-Gastaut syndrome. One case of benign neonatal-infantile epilepsy was characterized by neonatal onset seizures and episodic ataxia developed at the age of 78 months. Electroencephalograms at first visit of 5 cases showed that 2 cases were normal, 1 case had focal epileptic discharge, and 2 cases had multi-focal abnormal discharge with peak arrhythmia. The brain magnetic resonance imaging (MRI) of 3 cases were nomal, 1 case was abnormal (brain atrophy with decreased white matter) and the results of 1 case was unknown. The follow-up time ranged from 17 months to 89 months. Four cases of epilepsy were controlled and 1 case died at 2 years of age. Two cases had normal intelligence and motor development, 2 had moderate to severe intelligence retardation and motor critical state, and 1 had moderate to severe intelligence and motor development retardation. SCN2A gene variations were identified in all cases. There were 4 missense variations and 1 frameshift variation. Three variations had not been reported so far, including c.4906A>G,c.3643G>T,c.638delT. Conclusions: Variations in SCN2A gene can cause benign neonatal or infantile epilepsy and epileptic encephalopathy. Some children develop episodic ataxia with growing age. The variation of SCN2A gene is mainly missense variation.


Subject(s)
Ataxia/genetics , Child , Electroencephalography , Epilepsy/genetics , Female , Humans , Infant , Infant, Newborn , Male , Mutation , /genetics , Retrospective Studies , Spasms, Infantile/genetics
13.
Neuroscience Bulletin ; (6): 275-289, 2022.
Article in English | WPRIM | ID: wpr-929084

ABSTRACT

How to quickly predict an individual's behavioral choices is an important issue in the field of human behavior research. Using noninvasive electroencephalography, we aimed to identify neural markers in the prior outcome-evaluation stage and the current option-assessment stage of the chicken game that predict an individual's behavioral choices in the subsequent decision-output stage. Hierarchical linear modeling-based brain-behavior association analyses revealed that midfrontal theta oscillation in the prior outcome-evaluation stage positively predicted subsequent aggressive choices; also, beta oscillation in the current option-assessment stage positively predicted subsequent cooperative choices. These findings provide electrophysiological evidence for the three-stage theory of decision-making and strengthen the feasibility of predicting an individual's behavioral choices using neural oscillations.


Subject(s)
Aggression/physiology , Brain , Electroencephalography , Interpersonal Relations
14.
Article in English | WPRIM | ID: wpr-928587

ABSTRACT

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.


Subject(s)
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
15.
Article in English | WPRIM | ID: wpr-928577

ABSTRACT

Electroencephalography (EEG) monitoring is an important examination method in the management of critically ill neonates, which can be used to evaluate brain function and developmental status, severity of encephalopathy, and seizures and predict the long-term neurodevelopmental outcome of high-risk neonates with brain injury. EEG monitoring for neonates is different from that for adults and children, and its operation and interpretation are easily affected by the number of recording electrodes, electrode montage, and monitoring quality. Therefore, standard operation must be followed to ensure the quality of signal acquisition and correct interpretation, thereby ensuring proper management of critically ill neonates. The Subspecialty Group of Neonatology, Society of Pediatrics, Chinese Medical Association established an expert group composed of professionals in neonatology and brain electrophysiology to perform a literature review, summarize the minimum technical standards for neonatal EEG monitoring, and develop the expert consensus on minimum technical standards for neonatal EEG operation and report writing. This consensus will provide guidance for neonatal EEG operation, including technical parameters of EEG monitoring device, operation procedures of EEG monitoring, and specifications for report writing.


Subject(s)
Adult , Brain Injuries , Child , Consensus , Electroencephalography/methods , Humans , Infant, Newborn , Seizures , Writing
16.
Article in English | WPRIM | ID: wpr-928576

ABSTRACT

Neonatal electroencephalogram (EEG) monitoring guidelines have been published by American Clinical Neurophysiology Society, and the expert consensus on neonatal amplitude-integrated EEG (aEEG) has also been published in China. It is difficult to strictly follow the guidelines or consensus for EEG monitoring in different levels of neonatal units due to a lack of EEG monitoring equipment and professional interpreters. The Subspecialty Group of Neonatology, Society of Pediatrics, Chinese Medical Association, established an expert group composed of professionals in neonatology, pediatric neurology, and brain electrophysiology to review published guidelines and consensuses and the articles in related fields and propose grading management recommendations for EEG monitoring in different levels of neonatal units. Based on the characteristics of video EEG and aEEG, local medical resources, and disease features, the expert group recommends that video EEG and aEEG can complement each other and can be used in different levels of neonatal units. The consensus also gives recommendations for promoting collaboration between professionals in neonatology, pediatric neurology, and brain electrophysiology and implementing remote EEG monitoring.


Subject(s)
Child , Consensus , Electroencephalography , Humans , Infant, Newborn , Intensive Care Units, Neonatal , Neonatology , Seizures
17.
Article in Chinese | WPRIM | ID: wpr-928239

ABSTRACT

Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.


Subject(s)
Algorithms , Brain-Computer Interfaces , Electroencephalography , Evoked Potentials, Visual , Photic Stimulation
18.
Article in Chinese | WPRIM | ID: wpr-928238

ABSTRACT

Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.


Subject(s)
Brain/physiology , Brain-Computer Interfaces , Electroencephalography , Humans , Technology , User-Computer Interface
19.
Article in Chinese | WPRIM | ID: wpr-928225

ABSTRACT

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.


Subject(s)
Algorithms , Child , Deep Learning , Electroencephalography , Epilepsy/diagnosis , Humans , Seizures/diagnosis , Signal Processing, Computer-Assisted , Wavelet Analysis
20.
Article in Chinese | WPRIM | ID: wpr-928221

ABSTRACT

The research shows that personality assessment can be achieved by regression model based on electroencephalogram (EEG). Most of existing researches use event-related potential or power spectral density for personality assessment, which can only represent the brain information of a single region. But some research shows that human cognition is more dependent on the interaction of brain regions. In addition, due to the distribution difference of EEG features among subjects, the trained regression model can not get accurate results of cross subject personality assessment. In order to solve the problem, this research proposes a personality assessment method based on EEG functional connectivity and domain adaption. This research collected EEG data from 45 normal people under different emotional pictures (positive, negative and neutral). Firstly, the coherence of 59 channels in 5 frequency bands was taken as the original feature set. Then the feature-based domain adaptation was used to map the feature to a new feature space. It can reduce the distribution difference between training and test set in the new feature space, so as to reduce the distribution difference between subjects. Finally, the support vector regression model was trained and tested based on the transformed feature set by leave-one-out cross-validation. What's more, this paper compared the methods used in previous researches. The results showed that the method proposed in this paper improved the performance of regression model and obtained better personality assessment results. This research provides a new method for personality assessment.


Subject(s)
Algorithms , Brain , Electroencephalography/methods , Emotions , Humans , Personality Assessment
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