COMSTAT rule for vigilance classification based on spontaneous EEG activity.

The electro-encephalogram (EEG) and avoidance behavior of Rhesus monkeys with chronically implanted cortical electrodes, trained on a modified Sidman avoidance schedule, were studied following acute and chronic administration of ethyl alcohol. Acute intravenous administration of alcohol in doses of 0.5–2.0 g/kg produced blood alcohol levels of 50–250 mg/100 ml. As the blood alcohol level was raised, the impairment of avoidance responding was correlated with an increase in signs of central nervous system depression as measured by EEG patterns. A dose of 2.0 g/kg of alcohol was administered intravenously once daily until signs of behavioral deficit were minimal. Initially, avoidance behavior was abolished and spontaneous EEG activity was markedly slowed during the 30-min test period. When tolerance to the behavioral effects of alcohol occurred, the EEG pattern still showed high-voltage, slow-wave activity. This study suggests that tolerance to the behavioral effects of alcohol may be acquired more rapidly than central nervous system tolerance as measured by cortical EEG activity.

High resolution parametric description of slow wave sleep

Electrical status epilepticus during slow-wave sleep (ESES) is an EEG pattern of continuous (85-100%) diffuse epileptiform activity in the sleep EEG. The morphology of the epileptiform complexes is identical to benign epileptiform discharges of childhood (BEDC). Epilepsy with ESES (or “Penelope Syndrome”) is a form of age-dependent epileptic encephalopathies with the phenomenon of continuous spike-waves during slow wave sleep. This group of epilepsies also includes Pseudo-Lennox syndrome, Landau-Kleffner syndrome, autistic epileptiform regression and some others. In most cases, the ESES pattern correlates with the severity of cognitive deficit in this population of epileptic children. The aim of this study was to evaluate the efficacy and safety of levetiracetam in children with the ESES pattern in the EEG. Materials and Methods. During the period of 2010-2016, 34 epileptic children with ESES patterns in the EEG (14 boys and 20 girls) treated with levetiracetam (33 in combined and 1 in monotherapy) were studied. Results. Twenty eight of the patients with ESES patterns in the sleep EEG were diagnosed with the ESES form of epilepsy. Among them, 7 idiopathic, 13 symptomatic, and 8 “symptomatic” cases of ESES (the latter resulted from “double pathology” of idiopathic + hypoxic-ischemic factors and these children had cerebral palsy). Among other ESES patients, 3 cases were of Pseudo-Lennox syndrome, 2 cases of Landau-Kleffner syndrome and one girl with autistic epileptiform regression. All 28 children received levetiracetam at therapeutic doses of 20-80 mg/kg/daily. Levetiracetam was highly effective in 67.6% of patients (n=23); of those, 2 children showed full clinical and electroencephalographic remission before reaching puberty, 9 children had clinical remission and 12 children had a significant decrease in seizures and epileptiform discharges. A low efficacy of levetiracetam was seen in 20.6% (n=7) of patients. Disease aggravation (seizures and epileptiform discharges) was found in 11.8% (n=4) of patients. Other negative effects were observed in only 4 (11.8%) children (3 cases of agitation and sleep disturbance and one case of allergic rash). In conclusion, levetiracetam is a highly effective medication (67.6% of cases) in combined antiepileptic therapy in children with ESES. However, the risk of exacerbation was as high as 11.8%. The most effective combinations of levetiracetam were those with valproates and ethosuximide.

We have studied the interactions of reserpine and the monoamine oxidase inhibitors, nialamide and isocarboxazid, in rabbits. EEG recordings were made from animals with acute and chronically implanted electrodes. Gross behavioral observations were made in freely moving rabbits and brain amine concentrations of norepinephrine and serotonin were also determined. Emphasis was placed on observing drug effects over periods of time ranging up to 13 days. Rabbits given isocarboxazid or nialamide alone exhibit a phenomenon rarely seen in the control animals, namely partial activation, an EEG pattern in which slow waves of high amplitude are maintained in the cortex while fast activity appears in subcortical structures. This effect was also observed in rabbits treated with nialamide and reserpine in combination. By appropriate dosage schedules in which reserpine was administered to animals pretreated with monoamine oxidase inhibitors it was possible to maintain extended periods of frank arousal or EEG activation over a period of several days. The absolute concentrations of brain amines bore little or no relationship to the EEG effects observed, however, increased ratios of serotonin to norepinephrine were observed in conjunction with EEG activation and signs of behavioral excitement.

Objective: to establish the relationship between the presence of cognitive disorders in patients with arterial hypertension and changes in EEG, to assess the dynamics of these changes against the background of various modes of cerebroprotective therapy.Materials and methods: the study involved 92 people with arterial hypertension, whose average age was 63 ± 8.2 years. The research was carried out on the device “Encephalan-EEGR-19/26”. To assess cognitive functions, patients were tested using the MoСA test. Patients with cognitive impairment were divided into three groups of dynamic monitoring with diff erent modes of cerebroprotective therapy.Results: non-specifi c patterns in the slow-wave range were registered in patients with cognitive impairment during visual EEG analysis. In the quantitative analysis of the EEG revealed changes in the frequency and amplitude of the alpha rhythm, the power variation on the basic rhythms, the reduction of the total strength of the rhythms, the increase in relative power of slow rhythms in the frontal leads to the total power of the rhythms. After the treatment, most patients showed an increase in scores on the “Montreal scale”, a decrease in anxiety and depression on the” Hospital scale”, and an increase in the SF-36 index. Quantitative EEG analysis revealed positive dynamics comparable to the clinic and test data. The most favorable EEG dynamics was registered in groups of patients receiving neuroprotective and combined therapy.Conclusions: the results obtained indicate the diagnostic value of quantitative EEG analysis and the feasibility of adding drugs that improve the metabolism and blood supply to the brain to standard antihypertensive therapy.

Clinical Neurophysiology: EEG–Video Monitoring

Background: Reports on the effects of focal hemispheric damage on sleep EEG are rare and contradictory. Patients and Methods: Twenty patients (mean age ± SD 53 ± 14 years) with a first acute hemispheric stroke and no sleep apnea were studied. Stroke severity [National Institute of Health Stroke Scale (NIHSS)], volume (diffusion-weighted brain MRI), and short-term outcome (Rankin score) were assessed. Within the first 8 days after stroke onset, 1–3 sleep EEG recordings per patient were performed. Sleep scoring and spectral analysis were based on the central derivation of the healthy hemisphere. Data were compared with those of 10 age-matched and gender-matched hospitalized controls with no brain damage and no sleep apnea. Results: Stroke patients had higher amounts of wakefulness after sleep onset (112 ± 53 min vs. 60 ± 38 min, p < 0.05) and a lower sleep efficiency (76 ± 10% vs. 86 ± 8%, p < 0.05) than controls. Time spent in slow-wave sleep (SWS) and rapid eye movement (REM) sleep and total sleep time were lower in stroke patients, but differences were not significant. A positive correlation was found between the amount of SWS and stroke volume (r = 0.79). The slow-wave activity (SWA) ratio NREM sleep/wakefulness was lower in patients than in controls (p < 0.05), and correlated with NIHSS (r = –0.47). Conclusion: Acute hemispheric stroke is accompanied by alterations of sleep EEG over the healthy hemisphere that correlate with stroke volume and outcome. The increased SWA during wakefulness and SWS over the healthy hemisphere contralaterally to large strokes may reflect neuronal hypometabolism induced transhemispherically (diaschisis).

A source model is proposed to simulate spatial distributions of abnormal MEG and EEG activities, such as the delta activity associated with brain tumors. The brain tumor itself is electrically silent, but the spherical shell around the tumor may cause abnormal neural activities. The sources of these neural activities are represented by combinations of multiple dipoles. The magnetic fields and electrical potentials arising from these dipoles are calculated over the surface of the head. The results show that, in a special case where the dipoles are oriented in the same direction or are oriented radially, the spatial MEG and EEG patterns are analogous to those generated by a single dipole or a pair of dipoles. The electrical conductivity of brain lesions varies with such pathological circumstances as edemas and calcification. The effect of inhomogeneities of brain lesions on spatial MEG and EEG patterns is also studied. It is found that only the magnitude of the magnetic fields and potentials and influenced by the inhomogeneities, while the spatial patterns are not influenced.

ABSTRACTThis study examined the relationship between spontaneous skin potential responses (SSPRs) and the stages of sleep and waking in freely moving cats. Five adult cats were observed during 6 sleeping sessions of several hours each, permitting the analysis of several cycles of sleep in each session. Results showed a significant decrease in SSPR frequency through the different stages of vigilance, from waking, through drowsiness and slow wave sleep, to paradoxical sleep. During this last stage, spontaneous electrodermal activity was strongly reduced and few SSPRs appeared in bursts on a background of electrodermal silence. These results are discussed in relation to the known variations in spontaneous electrodermal activity in humans during sleep and in relation to the variations in some other physiological parameters during sleep.

This paper introduces a method of preference analysis based on electroencephalogram (EEG) analysis of prefrontal cortex activity. The proposed method applies the relationship between EEG activity and the Egogram. The EEG senses a single point and records readings by means of a dry-type sensor and a small number of electrodes. The EEG analysis adapts the feature mining and the clustering on EEG patterns using a self-organizing map (SOM). EEG activity of the prefrontal cortex displays individual difference. To take the individual difference into account, we construct a feature vector for input modality of the SOM. The input vector for the SOM consists of the extracted EEG feature vector and a human character vector, which is the human character quantified through the ego analysis using psychological testing. In preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band: θ, low-β, and high-β. To prove the effectiveness of the proposed method, we perform experiments using real EEG data. These results show that the accuracy rate of the EEG pattern classification is higher than it was before the improvement of the input vector.

EACMFS Prizes and Awards

Problem statement: The visual analysis of Electroencephalogram (EEG) activity has shown useful as a complementary tool in Alzheimer Disease (AD diagnosis) when the diagnosis remains uncertain, in addition to be used in some clinical protocols. However, this analysis is subject to the inherent equipment imprecision, biological artifact, electrical records, and subjective physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. Approach: In this study, it was used a new class of ANN, namely the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG activity and to assess its value as a possible complementary method for AD diagnosis. Thirty three AD patients and thirty four controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 Hz and 12.0 Hz (with an average frequency of 10.0 Hz), allowing a range of 0.5 Hz. Results: The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 80% of sensitivity and at 73% of specificity. Conclusion: Supported by results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG visual analysis and the ability of the PANN to deal in directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis.

Amplitude-Frequency EEG Mapping for Clinicist Support in Visual EEG Analysis

To the Editor: We read the article by Adams et al. [1] with great interest since we have more than a 4-year experience using the CIMON automated electroencephalographic (EEG) analysis program. As we have not experienced the high false-positive rate noted in their study, we were initially perplexed as to how their results were possible. This feeling is further highlighted by the work of Jonkman et al. [2], who compared the results of cerebral blood flow studies, conventional EEG analysis, and neurometric EEG analysis in patients having a history of stroke or transient ischemic attacks using univariate and multivariate parameters. With respect to accurate prediction of patients having a positive history of previous ischemic events, the neurometric EEG analysis approach showed a 50% improvement over visual analysis of conventional EEG. The false-positive rate derived from a concurrent control (normal) population was 3%. These results stand in stark contrast to those obtained for studies carried out under general anesthesia [2,3]. This prompts then a consideration of the sensitivity and specificity of the current implementation of the automated neurometric analytic approach used in the CIMON program. With regard to sensitivity, there is ample evidence (Harvey Edmonds, Department of Anesthesiology, University of Louisville Medical School, personal communication) that CIMON detects ischemia when an alternate method is used to herald its occurrence (i.e., transcranial infrared spectroscopy and transcranial Doppler). It is probable that Adams et al. failed to detect ischemia because at their time of measurement there was none. Thus, the methodology used by CIMON is probably not discredited on this account. Specificity, however, is another story. Their approach was to collect a baseline of single epochs arriving at an array of mean frequency band amplitudes. A standard deviation was accordingly calculated for each parameter mean based on an average of 24 single epochs. They then compared sliding averaged updates (consisting of 3, 10, and 20 averaged single epochs). As their epoch count increased from three to 20, their false-positives likewise increased (see Table 1in [1]). Arithmetically, the results represented in Table 1of the article by Adams et al. are anomalous. The mean value of relative delta lasting for a minimum of 5 min (the definition of cerebral dysfunction) taken across updated subsets consisting of sliding averages of 10 to 20 epochs cannot occur more frequently than the occurrences across a subset consisting of a sliding average of three epochs. This is because one is comparing the same EEG populations to each other (absence of ischemia, anesthetic, temperature, and ETCO2 change). As the number of epochs increases in the update (of any sample of sliding averages), its mean should be closer to the self-norm mean. We have simulated the authors' use of the CIMON program in a series of normal anesthetized patients and have confirmed their results. Their approach, however, differs from our use of the CIMON program, as we use sliding averages having the same number of epochs as the updates to generate a self-norm. In our experience, this markedly reduces the false-positives, which occur briefly, never lasting for more than 1-2 min. Such disparate results suggest a problem in the implementation of the software. The manufacturer of the software (Cadwell Laboratories, Inc., Kennewick, WA) has been advised of this problem and is working to correct it. Nevertheless, the CIMON program is an experimental implementation of an automated use of neurometric analysis, and studies like that by Adams et al. are important and useful for detecting errors in its computerized implementation. We therefore agree with the authors that caution is advised when using automated programs for analyzing EEG. This, however, in no way detracts from the neurometric approach; it detracts only from its current implementation. Lavern D. Gugino, PhD, MD Linda S. Aglio, MD Department of Anaesthesia Harvard Medical School Boston, MA 02115 E. Roy John, PhD Brain Research Laboratories New York, NY 10016

To study the clinical and electroencephalographic (EEG) characteristics of anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDAR encephalitis) in children. Retrospective analysis was performed on the clinical and EEG data of 105 patients with anti-NMDAR encephalitis treated in Beijing Children's Hospital (August 2011-March 2015). Of the 105 patients, 38 were male and 67 were female.The age of onset was from 6 months and 26 days to 15 years and 8 months (average (8±4)years). The time for confirmed diagnosis was from 4 days to 850 days (median 24.5 days). According to the modified Rankin scales, the patient's clinical conditions were assessed and underwent continuous EEG (cEEG) monitoring.The data were reviewed and analyzed. Based on the severity of the disease, the 105 patients were divided into three groups: mild group (12 cases), moderate group (65 cases), and severe group (28 cases). There were 91 cases(86.7%)with abnormal EEG patterns, including 28 cases (26.7%) with slow background activity in EEG, 25 cases (23.8%) with generalized or diffuse slow waves, 33 cases (31.4%) had focal slow waves, 41 cases (39.0%) had epileptic waves; 10 cases (9.5%) showed unilateral or diffuse alpha-theta band rhythms in nonrapid eye movement (NREM) sleep, 7 cases (6.7%) showed extreme delta brush waves (EDB). Accordingly, the number of patients with abnormal EEG in mild, moderate and severe groups was 5, 58 (89.2%) and 28(100.0%). Seven patients with EDB phenomenon were all in the severe group, and 10 patients with abnormal alpha-theta band rhythms were in the moderate group. In children with anti-NMDAR encephalitis, the EEG patterns are in line with the changes of EEG in general encephalitis.The extent of EEG abnormalities correlates with the clinical severity of the disease. Extreme delta brush and alpha-theta band rhythms may be suggestive of diagnosis and clinical assessment of the disease.