Hallazgos electroencefalográficos compatibles con encefalopatía en pacientes ingresados por covid-19 / Electroencephalographic findings compatible with encephalopathy in patients admitted for COVID-19

RESUMEN INTRODUCCIÓN: La covid-19 afecta principalmente al aparato respiratorio, sin embargo, también se ha descrito afectación tanto directa como indirecta en el sistema nervioso central y periférico, lo cual ocasiona una gran variedad de manifestaciones neurológicas, siendo la encefalopatía una de las más frecuentemente observadas. OBJETIVO: Se busca mostrar la utilidad del video-electroencefalograma (vEEG) en el diagnóstico de encefalopatía en pacientes ingresados por covid-19, así como su valor para determinar el pronóstico de estos pacientes. MÉTODOS: Estudio observacional retrospectivo con 76 vEEG de 41 pacientes con covid-19 confirmada. Los estudios se han realizado entre los meses de marzo del 2020 y junio del 2021. Se estudió la gravedad de la enfermedad, así como sus características clínicas y neurológicas, el tratamiento farmacológico y los hallazgos electroencefalográficos según el grado de disfunción de la encefalopatía que desarrollaron estos pacientes. RESULTADOS: De los 41 pacientes, 12 (29 %) presentaron signos electroencefalográficos de disfunción cerebral leve, 15 (37 %) disfunción cerebral moderada y 14 (34 %) disfunción cerebral severa, los cuales se asociaron con una mayor mortalidad. CONCLUSIONES: En los 76 vEEG realizados a los 41 pacientes ingresados con encefalopatías asociadas con infección por covid-19, no se observó un patrón distinto a los descritos en encefalopatías de otras etiologías. El vEEG fue útil para confirmar la sospecha clínica de una disfunción cerebral en pacientes con encefalopatías asociadas con infección por covid-19 y para asignarle un grado de severidad, confirmando su beneficio como biomarcador diagnóstico y pronóstico.

ABSTRACT INTRODUCTION: COVID-19 mainly affects the respiratory system; however, both direct and indirect involvement of the central and peripheral nervous system has also been described, causing a wide variety of neurological manifestations, with encephalopathy being one of the most frequently observed neurological manifestations. OBJECTIVE: With this article we intend to show the usefulness of vEEG in the diagnosis of encephalopathy in patients referred for COVID-19 who develop this neurological complication, as well as its value in determining the prognosis of these patients. METHODS: Retrospective observational study with 76 video-electroencephalograms of 41 patients with confirmed COVID-19 infection. The studies were performed during the months of March 2020 through June 2021. Disease severity, clinical and neurological features, pharmacological treatment and electroencephalographic indings were studied according to the degree of encephalopathy dysfunction these patients developed. RESULTS: Of the 41 patients, 12 (29 %) presented electroencephalographic signs of mild cerebral dysfunction, 15 (37 %) moderate cerebral dysfunction, and 14 (34 %) severe cerebral dysfunction, which were associated with higher mortality. CONCLUSIONS: In the 76 vEEG performed in the 41 patients admitted with encephalopathies associated with COVID-19 infection, no pattern different from that described in encephalopathies of other etiologies was observed. The vEEG was useful to confirm the clinical suspicion of brain dysfunction in patients with encephalopathies associated with COVID-19 infection and to assign a degree of severity, confirming its benefit as a diagnostic and prognostic biomarker.

Adaptive smith predictor controller for total intravenous anesthesia automation

Anesthetic agent propofol needs to be administered at an appropriate rate to prevent hypotension and postoperative adverse reactions. To comprehend more suitable anesthetic drug rate during surgery is a crucial aspect. The main objective of this proposal is to design robust automated control system that work effi ciently in most of the patients with smooth BIS and minimum variations of propofol during surgery to avoid adverse post reactions and instability of anesthetic parameters. And also, to design advanced computer control system that improves the health of patient with short recovery time and less clinical expenditures. Unlike existing research work, this system administrates propofol as a hypnotic drug to regulate BIS, with fast bolus infusion in induction phase and slow continuous infusion in maintenance phase of anesthesia. The novelty of the paper lies in possibility to simplify the drug sensitivity-based adaption with infusion delay approach to achieve closedloop control of hypnosis during surgery. Proposed work uses a brain concentration as a feedback signal in place of the BIS signal. Regression model based estimated sensitivity parameters are used for adaption to avoid BIS signal based frequent adaption procedure and large off set error. Adaptive smith predictor with lead–lag fi lter approach is applied on 22 diff erent patients' model identifi ed by actual clinical data. The actual BIS and propofol infusion signals recorded during clinical trials were used to estimate patient's sensitivity parameters EC50 and λ. Simulation results indicate that patient's drug sensitivity parameters based adaptive strategy facilitates optimal controller performance in most of the patients. Results are obtained with proposed scheme having less settling time, BIS oscillations and small off set error leads to adequate depth of anesthesia. A comparison with manual control mode and previously reported system shows that proposed system achieves reduction in the total variations of the propofol dose. Proposed adaptive scheme provides better performance with less oscillation in spite of computation delay, surgical stimulations and patient variability. Proposed scheme also provides improvement in robustness and may be suitable for clinical practices.

Sleep-Aids Derived from Natural Products

Although drugs such as barbiturates and benzodiazepines are often used for the treatment of insomnia, they are associated with various side effects such as habituations, tolerance and addiction. Alternatively, natural products with minimal unwanted effects have been preferred for the treatment of acute and/or mild insomnia, with additional benefits of overall health-promotion. Basic and clinical researches on the mechanisms of action of natural products have been carried out so far in insomnia treatments. Recent studies have been focusing on diverse chemical components available in natural products, with an interest of developing drugs that can improve sleep duration and quality. In the last 15 years, our co-workers have been actively looking for candidate substances from natural products that can relieve insomnia. This review is, therefore, intended to bring pharmacological data regarding to the effects of natural products on sleep duration and quality, mainly through the activation of GABAA receptors. It is imperative that phytochemicals will provide useful information during electroencephalography (EEG) analysis and serve as an alternative medications for insomnia patients who are reluctant to use conventional drugs.

Clinical value of active electroencephalography in attention-deficit hyperactivity disorder / 临床儿科杂志

Objective To explore the value of active electroencephalography (AEEG) in the diagnosis of attention-deficit hyperactivity disorder.Methods Routine EEGs were performed in 179 patients with attention-deficit hyperactivity disorder.AEEGs were performed in 117 patients who have been performed routine EEGs.Results Abnormal discharge was observed in 17 cases in routine EEGs and epileptiform activities were found in 6 patients.The abnormal rate is 9.5％.Abnormal discharge was observed in 21 cases in AEEGs and epileptiform activities were found in 12 patients.The abnormal rate is 17.9％.Conclusions AEEG is an important clinical examination which also serves as a reference for brain function and epilepsy.AEEG could help judge the prognosis of epilepsy comorbiding with attention-deficit hyperactivity disorder.

MEG and EEG dipole clusters from extended cortical sources

Data from magnetoencephalography (MEG) and electroencephalography (EEG) suffer from a rather limited signal-to-noise-ratio (SNR) due to cortical background activities and other artifacts. In order to study the effect of the SNR on the size and distribution of dipole clusters reconstructed from interictal epileptic spikes, we performed simulations using realistically shaped volume conductor models and extended cortical sources with different sensor configurations. Head models and cortical surfaces were derived from an averaged magnetic resonance image dataset (Montreal Neurological Institute). Extended sources were simulated by spherical patches with Gaussian current distributions on the folded cortical surface. Different patch sizes were used to investigate cancellation effects from opposing walls of sulcal foldings and to estimate corresponding changes in MEG and EEG sensitivity distributions. Finally, white noise was added to the simulated fields and equivalent current dipole reconstructions were performed to determine size and shape of the resulting dipole clusters. Neuronal currents are oriented perpendicular to the local cortical surface and show cancellation effects of source components on opposing sulcal walls. Since these mostly tangential aspects from large cortical patches cancel out, large extended sources exhibit more radial components in the head geometry. This effect has a larger impact on MEG data as compared to EEG, because in a spherical head model radial currents do not yield any magnetic field. Confidence volumes of single reconstructed dipoles from simulated data at different SNRs show a good correlation with the extension of clusters from repeated dipole reconstructions. Size and shape of dipole clusters reconstructed from extended cortical sources do not only depend on spike and timepoint selection, but also strongly on the SNR of the measured interictal MEG or EEG data. In a linear approximation the size of the clusters is proportional to the inverse SNR.

Functional Imaging of Tinnitus / 한양의대학술지

Tinnitus is an auditory phantom characterized by the perception of sound without the presence of an external acoustical source. The peripheral auditory system is considered to contribute to the initiation of tinnitus but only explains the severity and distress level to a limited degree. The neuropsychological models of tinnitus have been developed to explain the pathophysiology of tinnitus as a malfunctioning feedforward/feedback signal in the central neural system including the auditory brainstem, limbic system, auditory cortices, and other anatomical features. Functional neuroimaging techniques have been introduced in recent decades and have provided non-invasive tools to assess the working human brain in vivo. Researchers have found these techniques valuable in examining the neural correlates of tinnitus and have been able to not only support the neuropsychological model but to expand it. Though neuroimaging studies on tinnitus only began in 1990s, they have been increasing exponentially in number. In this review, we investigate the current state of functional neuroimaging studies on tinnitus in humans. The characteristics of commonly used functional neuroimaging techniques including positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG) and magnetoencephalography (MEG) are also discussed. We briefly review recent studies on the tinnitus-brain relationship that have used those research tools.

The study of etiological profile in new onset seizures in Indian scenario.

A seizure (Latin word which means “to take possession of”) is a paroxysmal event due to abnormal excessive or synchronous neuronal activity in the brain. Seizure is a medical emergency and about 1 in 10 persons will experience a seizure in their lifetime. Etiological contribution to seizures in developing countries is different from developed countries. Epilepsies related to malaria, neuroinfections, tuberculosis, HIV, meningitis, trauma and perinatal difficulties more prevalent in India and other developing countries. Neurocysticercosis is the most common cause of seizures/epilepsy in the developing countries and designated as a “biological marker” of the social and economic development of a community. In India, Single Small Enhancing CT Lesions (SSECTL) being the most common radiological finding and dying cysticercus larva in histopathological studies. Aim: To study the etiological profile in new onset seizures. Methods: This was an observational and prospective study. The present study enrolled 100 patients above 15 years of age with new onset seizures. All the patients and their relatives were interviewed regarding history and thorough clinical examination was done. Routine blood investigations, blood urea, serum creatinine, blood sugar, liver function tests, serum electrolyte were done. Special investigations like electroencephalography (EEG), CT scan brain, MRI, and lumbar puncture were done in selected cases. Results: Out of 100 patients included in the study, neuroinfection is leading cause of seizure in 36%, Cerebrovascular accidents (25%) and metabolic in (12%). Majority of seizures in neuroinfections were due to neurocysticercosis in 15 patients (42%) followed by meningoencephalitis in 14 patients (38%). Among Cerebrovascular accidents, stroke accounted for 84% (21) (Infarct-12, Haemorrhage-9), followed by cerebral venous thrombosis 12% (3). Out of 12 patients with metabolic seizures, hypoglycaemia and hyponatremia constituted 33% each. Conclusions: Etiological spectrum of seizures includes neuroinfection, CVA, tumour, metabolic, poisoning and alcohol withdrawal. Neuroinfection accounted for significant number of seizures in all the age groups. Neurocysticercosis is the most common etiology among neuroinfections. Cerebrovascular accidents common in 4th & 5th decades. Limitation: Patients <15 years with new onset seizures were not included in the study.

Predictive Factors of Postneonatal Epilepsy: The Significance of Follow-up EEG Changes / 대한소아신경학회지

PURPOSE: To describe the prevalence and severity of postneonatal epilepsy after neonatal seizures in term neonates as well as to evaluate the predictive factors of postneonatal epilepsy. METHODS: Retrospective analysis of 50 children who experienced neonatal seizures. In children with at least 12 months of follow-up data, the univariate and multivariate logistic regression analysis was applied in order to determine the predictive factors of postneonatal epilepsy. Electroencephalography (EEG), neuroimaging studies, and other clinical variables were systematically analyzed. Infants with abnormal EEG recordings in the initial studies underwent a follow-up EEG examination at 1 or 3 months later. RESULTS: Twelve of 50 neonates with neonatal seizures (24%) developed postneonatal epilepsy. Seventy-five percent (9 of 12) of the children with postneonatal epilepsy were eventually seizure-free without AED (antiepileptic drug), and 25% (3 of 12) had seizures at the last follow-up (modified angel classification 2 or 3, mean follow-up period; 52 months). On the univariate logistic regression analysis, abnormal EEG, Magnetic resonance imaging (MRI) findings, combined with encephalopathy and the number of AEDs were correlated with postneonatal epilepsy (P<0.05). On the multivariate analysis, the persistent abnormality on the follow-up EEG was correlated with postneonatal epilepsy (adjusted odds ratio=20.78; P=0.016). CONCLUSION: The number of intractable cases was relatively low, indicating good prognosis in postneonatal epilepsy. Abnormal EEG, MRI findings, combined with encephalopathy, and the number of AEDs were very good predictors of postneonatal epilepsy. The persistent abnormality in the follow-up EEG was more frequently seen in postneonatal epilepsy patients.

Análisis de señales electroencefalográficas para la clasificación de habla imaginada / Electroencephalographic Signals Analysis for Imagined Speech Classification

El presente trabajo tiene como objetivo interpretar las señales de EEG registradas durante la pronunciación imaginada de palabras de un vocabulario reducido, sin emitir sonidos ni articular movimientos (habla imaginada o no pronunciada) con la intención de controlar un dispositivo. Específicamente, el vocabulario permitiría controlar el cursor de la computadora, y consta de las palabras del lenguaje español: "arriba", "abajo", "izquierda", "derecha", y "seleccionar". Para ello, se registraron las señales de EEG de 27 individuos utilizando un protocolo básico para saber a priori en qué segmentos de la señal la persona imagina la pronunciación de la palabra indicada. Posteriormente, se utiliza la transformada wavelet discreta (DWT) para extraer características de los segmentos que son usados para calcular la energía relativa wavelet (RWE) en cada una de los niveles en los que la señal es descompuesta, y se selecciona un subconjunto de valores RWE provenientes de los rangos de frecuencia menores a 32 Hz. Enseguida, éstas se concatenan en dos configuraciones distintas: 14 canales (completa) y 4 canales (los más cercanos a las áreas de Broca y Wernicke). Para ambas configuraciones se entrenan tres clasificadores: Naive Bayes (NB), Random Forest (RF) y Máquina de vectores de soporte (SVM). Los mejores porcentajes de exactitud se obtuvieron con RF cuyos promedios fueron 60.11% y 47.93% usando las configuraciones de 14 canales y 4 canales, respectivamente. A pesar de que los resultados aún son preliminares, éstos están arriba del 20%, es decir, arriba del azar para cinco clases. Con lo que se puede conjeturar que las señales de EEG podrían contener información que hace posible la clasificación de las pronunciaciones imaginadas de las palabras del vocabulario reducido.

This work aims to interpret the EEG signals associated with actions to imagine the pronunciation of words that belong to a reduced vocabulary without moving the articulatory muscles and without uttering any audible sound (imagined or unspoken speech). Specifically, the vocabulary reflects movements to control the cursor on the computer, and consists of the Spanish language words: "arriba", "abajo", "izquierda", "derecha", and "seleccionar". To do this, we have recorded EEG signals from 27 subjects using a basic protocol to know a priori in what segments of the signal a subject imagines the pronunciation of the indicated word. Subsequently, discrete wavelet transform (DWT) is used to extract features from the segments. These are used to compute relative wavelet energy (RWE) in each of the levels in that EEG signal is decomposed and, it is selected a RWE values subset with the frequencies smaller than 32 Hz. Then, these are concatenated in two different configurations: 14 channels (full) and 4 channels (the channels nearest to the brain areas of Wernicke and Broca). The following three classifiers were trained using both configurations: Naive Bayes (NB), Random Forest (RF) and support vector machines (SVM). The best accuracies were obtained by RF whose averages were 60.11% and 47.93% using both configurations, respectively. Even though, the results are still preliminary, these are above 20%, this means they are more accurate than chance for five classes. Based on them, we can conjecture that the EEG signals could contain information needed for the classification of the imagined pronunciations of the words belonging to a reduced vocabulary.

Valproic Acid-Induced Hyperammonemic Encephalopathy Initially Misdiagnosed as Nonconvulsive Status Epilepticus

Valproic acid-induced hyperammonemic encephalopathy (VHE) is a very rare but serious complication. Discontinuation of valproic acid is the first and critical step for treatment. VHE can occur in people with normal liver function, despite normal doses and serum levels of valproic acid, therefore it is very hard to predict. Recently, we experienced a case of VHE. Here we will present the clinical, laboratory and electroencephalography findings in this patient.

EEG Analysis of the Left and Right brain activities from cartoon learning versus text learning / 대한의료정보학회지

OBJECTIVE: Cartoons have been known to motivate learners and make learning process easier by combining verbal and visual effects. But they are mostly applied to motivate the less able learners, and have limits in delivering comprehensive information. Thus, more careful and scientific validation for the pros and cons of using cartoons for everyday use in various subjects is in need. METHODS: In this research, we used Electroencephalography(EEG) to compare cartoon learning and text learning by measuring four characteristic brainwaves including theta, alpha, sensory motor rhythms(SMR), and beta, from the left and right brain. The EEG signals acquired from 24 subjects are analyzed using the mean difference of the left and right brain and canonical correlation analysis. RESULTS: The theta brainwave of the left brain and right brain shows significant differences (p<0.05) from cartoon learning versus text learning in the theta brainwave while the other brain waves show similar patterns. CONCLUSION: Cartoon learning produced significantly stronger theta brainwaves than text learning implicating that cartoon learning reduces more focused attention, SMR brainwaves and beta brainwaves from the left brain explained cartoon learning and text learning process while alpha brainwaves explained those processes in the right brain.

Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines / 西安交通大学学报·英文版

Mental task classification is one of the most important problems in Brain-computer interface. This paper studies the classification of five-class mental tasks. The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM (support vector machines). The averaged classification accuracy of 85. 6% over 7 subjects was achieved for 2-second EEG segments. And the results for EEG segments of 0. 5s and 5. 0s compared favorably to those of Garrett's. The results indicate that the parameter of mean period represents mental tasks well for classification. Furthermore, the method of mean period is less computationally demanding, which indicates its potential use for online BCI systems.

Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines / 药物分析学报

Mental task classification is one of the most important problems in Brain-computer interface. This paper studies the classification of five-class mental tasks. The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM (support vector machines). The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments. And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's. The results indicate that the parameter of mean period represents mental tasks well for classification. Furthermore, the method of mean period is less computationally demanding, which indicates its potential use for online BCI systems.

Effect of different attention states on auditory ERP and EEG power / 中华物理医学与康复杂志

Objective To study the effect of different attention states on auditory ERP and EEG power in normal subjects. Methods In three different states such as passive attention,active attention with target counting and active attention without target counting,EEG were recorded using oddball paradigm in 8 normal subjects while the test tones were presented;Then the peak-peak amplitude and latency of ERP were evaluated and the EEG power spectra were observed. Results The condition of active attention with target counting resulted in the smallest peak-peak amplitude of P 2-N 1 and the largest one of P 3-N 2,and it led to the increment of EEG power at the frequency of 10Hz,while the latencies did not differ among the three different attention states. Conclusion Reduced P 2-N 1 amplitude and increased P 3-N 2 amplitude reflected the allocation changes of attention resource in different states,and increased EEG power reflected more energy consumption in the process of recognition and memory,but the latency was relatively stable and not affected significantly by subject's attention condition.

Basic Electrophysiology of the Electroencephalography

Although neuroimaging techniques and other diagnostic procedures has been developed, electroencephalography(EEG) is still very important for the evaluation of various brain diseases and functional studies of human brain. EEG is formed mainly by spatial and temporal summations of postsynaptic potentials generated from a large population of pyramidal cells that can be considered as a collection of oscillating dipoles. EEG shows continuous rhythmic oscillation depending on sleep-waking state. Alpha rhythms are generated in cortical areas acting as epicenters with local spread, although the precise cellular mechanism is still unknown. It's been known that neurons in the nucleus reticular thalami are the pacemakers of sleep spindle. Alterations in the circuit of the reticular nuclei-thalamocortical relay neuron-cortical neuron are responsible for generalized spike and wave complexes. At the intracellular level, large paroxysmal depolarizing shifts produce focal epileptic spikes. Slow waves of EEG appear to be related to thalamocortical and/or corticothalamic deafferentation. The interpretation of routine EEG requires a well training from a qualified EEG teacher and reading adequate amount of EEG under supervision. Frequent misinterpretations of routine EEG have been observed in both local clinics and general hospitals. The most common findings of normal routine EEG misinterpreted as abnormal are normal variants and artifacts of various sources. There are considerable variations of normal EEG rhythms and pseudoepileptiform discharges. Eyeball movements produce prominent or subtle EEG changes over the frontal regions that are sometimes hard to be differentiated from abnormal slow waves over that region. Systematic approach was described for a good interpretation of routine EEG.

Comparison of Electroencephalography, Neuroimaging Studies and Surgical Outcome between Mesial and Neocortical Temporal Lobe Epilepsies / 대한간질학회지

PURPOSE: To compare the diagnostic value of electroencephalography (EEG), MRI and PET studies and surgical outcome in patients with medically refractory temporal lobe epilepsy due to hippocampal sclerosis (HS) versus temporal lobe lesions (TLL). METHODS: Records of 122 consecutive patients who underwent surgery for epilepsy from January 1993 to April 2000 were retrieved from the MGH Epilepsy Surgery Database. Fifty eight patients with temporal lobe epilepsy due to pathologically proven HS or TLL were identified and presurgical interictal and ictal EEG, MRI, and 2-[(18)F]fluoro-2-deoxy-D-glucose (FDG)-PET data and surgical outcome were reviewed. Patients with dual or normal pathology were excluded. Pathologically proven HS was present in 32 patients, and 26 patients has temporal lobe lesions (cortical dysplasia in 6 patients, vascular malformation in 6, gliomas in 5, DNET in 4, heterotopia in 1, other pathologies in 4). Comparisons of the diagnostic value of EEG, MRI and FDG-PET studies were performed in 43 patients who were seizure-free after epilepsy surgery. Among 43 patients, HS was in 24 patients and TLL in 19. RESULTS: The occurrence of abnormal interictal and ictal EEG, MRI and FDG-PET findings in the side of operation was not significantly different between patients with HS and with TLL respectively. There was no significant difference in at least one year follow-up surgical outcome between the two groups. CONCLUSIONS: Diagnostic value of presurgical interictal and ictal EEG, MRI and FDG-PET findings, and surgical outcome were not different in patients with mesial versus neocortical temporal lobe epilepsies.

The Realization of Early Diagnostic System for Cerebral Ischemia Using Compressed Spectral Array(CSA)

The trials to detect the cerebral ischemia during the brain surgery have been continued since last three decades. The intraoperative Xenon isotope cerebral blood flow(CBF) measurement and EEG monitoring were proven to be useful techniques for this purpose. But these techniques have several drawbacks and are not easily applicable in most institutions. Authors, therefore, developed a intraoperative cerebral ischemia monitoring system which applied the digital electroencephalography(EEG) and compressed spectral array(CSA) technique. Technical details of our system and the examples of clinical applications are described.

Research on control system of brain-computer interface based on the alpha waves in EEG / 医疗卫生装备

A system design of brain-computer interface based on the alpha waves in human electroencephalography(EEG) is presented in this paper.With the effects on the alpha wave amplitudes of human eye's open and close involved in,the selection control of four direction targets can be performed on a computer screen.The system speed and accuracy rate are investigated through the experiments involving 5 subjects.It is shown that the system is easy to operate and needs no complex learning and biofeedback training.The studying results provide a good technical foundation for the development of BCI control panel and the realization of the system integration.It has the potential application for clinical engineering and is valuable for further research.