Flat EEG Research Articles

Optimization of inhaled anesthesia for Octodon degus using electroencephalography.

Physiological responses to inhaled anesthetics vary among species. Therefore, a precise anesthetic technique is important for each individual species. In this study, we focused on the degu (Octodon degus), a small herbivorous rodent. Degus have recently begun to be used as laboratory models for brain research because of certain human-like characteristics, such as spontaneous development of Alzheimer's disease. In this study, we evaluated appropriate induction and maintenance anesthesia conditions for isoflurane and sevoflurane in degus by a stimulation test, electroencephalography (EEG), minimum alveolar concentration (MAC), and vital signs. During induction, more rapid time to loss of the righting reflex and deeper anesthesia in degus were observed in isoflurane. The MAC value for degus were 1.75 ± 0.0% in isoflurane and 2.25 ± 0.27% in sevoflurane. Whereas some degus were awake during maintenance anesthesia using both anesthetics at concentrations of ≤2%, no rats were awake when using sevoflurane at a concentration of 2%. The duration of the total flat EEG, a measure of the depth of maintenance anesthesia, was longer for isoflurane than for sevoflurane. Furthermore, higher concentrations of both anesthetics suppressed the respiratory rate in degus. These new findings regarding inhalation anesthesia in degus will contribute to future developments in the fields of laboratory animals and veterinary medicine.

Spectral EEG correlations from the different phases of general anesthesia.

Electroencephalography (EEG) signals contain transient oscillation patterns commonly used to classify brain states in responses to action, sleep, coma or anesthesia. Using a time-frequency analysis of the EEG, we search for possible causal correlations between the successive phases of general anesthesia. We hypothesize that it could be possible to anticipate recovery patterns from the induction or maintenance phases. For that goal, we track the maximum power of the α-band and follow its time course. We quantify the frequency shift of the α-band during the recovery phase and the associated duration. Using Pearson coefficient and Bayes factor, we report non-significant linear correlation between the α-band frequency and duration shifts during recovery and the presence of the δ or the α rhythms during the maintenance phase. We also found no correlations between the α-band emergence trajectory and the total duration of the flat EEG epochs (iso-electric suppressions) induced by a propofol bolus injected during induction. Finally, we quantify the instability of the α-band using the mathematical total variation that measures possible deviations from a flat line. To conclude, the present correlative analysis shows that EEG dynamics extracted from the initial and maintenance phases of general anesthesia cannot anticipate both the emergence trajectory and the extubation time.

Suitability of electroencephalography in brain death determination: a monocentric, 10-year retrospective, observational investigation of 428 cases

BackgroundWe aimed to verify the usefulness of electroencephalographic (EEG) activity recording (that is mandatory according to the Italian law), in addition to two clinical evaluations spaced 6 h, among the procedures of brain death determination (BDD) in adult individuals.MethodsThe study is a monocentric, retrospective analysis of all BDDs performed in the last 10 years at Policlinico Le Scotte in Siena (Italy).ResultsOf the 428 cases revised (mean age 67.6 ± 15.03 years; range 24–92 years), 225 were males and 203 females. In total, 212 out of 428 patients (49.5%) were donors. None of the BDD procedures were interrupted due to the reappearance of EEG activity (neither for clinical reasons) at any sampling time, with the exception of one case that was considered a false negative at critical reinspection of the EEG. In 6/428 cases (1.4%), a cardiac arrest occurred during the 6 h between the first and second evaluation, thus missing the opportunity to take organs from these patients because the BDD procedure was not completed.ConclusionsOnce the initial clinical examination before convening the BDD Commission has ascertained the absence of brainstem reflexes and of spontaneous breathing, and these clinical findings are supported by a flat EEG recording, the repetition of a 30-min EEG twice over a 6 h period seems not to add additional useful information to clinical findings. Current data, if confirmed in other centers and possibly in prospective studies, may help to promote a scientific and bioethical debate in Italy, as well as in other countries where the EEG is still mandatory, for eventually updating the procedures of BDD.

Flat EEG Image Segmentation by Fuzzy Entropy-Based Multi-Level Thresholding

Thresholding is a type of image segmentation that deals with the conversion of an image with many gray levels into another image with fewer gray levels. It classifies grayscale pixels into two categories which creates a binary image. However, the output image is not alwayssatisfying due to several factors such as inherent image vagueness as uncertainty arises within the gray values of an image. In this paper, a multi-level image thresholding based on fuzzy entropy is applied on Flat Electroencephalography (Flat EEG) image. The outcomes are compared visually with global thresholding.

Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods

BackgroundTo compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts.MethodsA total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as “good” (Cerebral Performance Category 1–2) or “poor” (Cerebral Performance Category 3–5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG.ResultsThe deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44–64%) at a false positive rate (FPR) of 0% (95% CI 0–2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52–100%) at a FPR of 12% (95% CI 0–24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83–83%) at a FPR of 3% (95% CI 3–3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels.ConclusionsA deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.

TIMES REQUIRED FOR EACH STEP OF BRAIN DEATH DONOR MANAGEMENT

Background: Delayed brain death determination or prolonged time period for donor management cause increasing number of donor death, or withdrawal of family’s consent for donation, and increasing the expenditure of intensive care expenses. In order to prepare a plan to minimize the donation failure by understanding the times required for each step of organ donation is urgent. Methods: From January 2016 to December 2018, we reviewed KODA statistical data of reported potential brain death and investigated the time intervals that take to complete each process of brain death management. Organ donation procedures were divided into five steps. The first step started from the end of the first brain death examination to the end of the second examination, the second step was from finishing the second brain death examination until we got the flat EEG, the third stage was from finishing EEG test to the declaration of brain death by brain death committee, and the fourth stage was from end of committee to entrance to operating room and the fifth step was from the operating room entrance to the completion of organ procurement. Results: During the study period, 1,633 cases of potential brain death were managed for organ donation, and we analyzed 1,594 cases excluding donors under age six. The average time spent on organ donation procedure was 33 hours and 16 minutes. The time required for each step was 11 hours 28 minutes for step1, 5 hours 15 minutes for step2, 5 hours 58 minutes for step3, 6 hours 38 minutes for step4 and 3 hours 42 minutes for step5. Among the analyzed cases, 119 cases spent more than 48 hours from step 1 to 4, and 63 cases those included holidays in the donation process took an average of 71 hours and 5 minutes. Ninety three cases were discontinued during the donation procedure and their causes of interruption were poor organ quality 15 cases, patient death in 15, failure in brain death judgement in 13 and withdrawal of donation consent in six. About time of interruption of donation procedures, 45 cases were stopped at step 1 and 2, and the total times spent for step 1 and 2 was 16 hours and 43 minutes which accounting for half of all brain death donation procedures. In particular, the first step took 11 hours and 28 minutes on average, almost twice as long as the 6 hour legal inspection interval. The third step took an average of six hours, which is unnecessary in foreign countries where there is no brain death judgment committee. Conclusion: Brain death determination procedures should be accurate but not unnecessarily delayed. Among the measures to reduce the time required, improvement of the execution of the first and second brain death examinations which repeat the same test, improvement of delay of the EEG examination to obtain flat wave, and careful approach for abolishing the brain death judgment committee are necessary. Delayed brain death organ donation procedure caused by lack of manpower especially during holidays and night time need a government level policy revision and social consensus.

Differentiating motor phenomena in tilt-induced syncope and convulsive seizures.

We assessed motor phenomena in syncope and convulsive seizures to aid differential diagnosis and understand the pathophysiologic correlates. We studied video-EEG recordings of tilt-induced syncope and convulsive seizures in participants aged 15 years and older. Syncope was defined as (1) loss of consciousness (video-assessed), (2) circulatory changes (accelerating blood pressure decrease with or without bradycardia/asystole), and (3) EEG changes ("slow" or "slow-flat-slow"). We assessed myoclonic jerks and tonic postures of the arms and noted time of occurrence, laterality, synchrony, and rhythmicity (mean consecutive differences of interclonic intervals). Video-EEG records of 65 syncope cases and 50 convulsive seizures were included. In syncope, postures occurred in 42 cases (65%) and jerks in 33 (51%). Fewer jerks occurred in syncope (median 2, range 1-19) compared to convulsive seizures (median 48, range 20-191; p < 0.001). Jerks were more rhythmic in seizures compared to syncope (p < 0.001). Atonia was seen in all syncope cases, while this was not observed in any seizure. Jerks predominantly occurred during the slow and postures during the flat EEG phase. Jerks and tonic postures were common in syncope, but semiology differed from convulsive seizures. The lack of overlap in the number of jerks suggests that less than 10 indicates syncope and more than 20 a convulsive seizure: the "10/20 rule." Loss of tone strongly favors syncope. The EEG correlates imply that jerks in syncope are likely of cortical origin, whereas tonic postures may result from brainstem disinhibition.

Fuzzy topological digital space and their properties of flat electroencephalography in epilepsy disease

There are an abnormal electric activities or irregular interference in brain of epilepsy patient. Then a sensor will be put in patient’s scalp to measure and records all electric activities in brain. The result of the records known as Electroencephalography (EEG). The EEG has been transfer to flat EEG because it’s easier to analyze. In this study, the uncertainty in flat EEG data will be considered as fuzzy digital space. The purpose of this research is to show that the flat EEG is fuzzy topological digital space. Therefore, the main focus for this research is to introduce fuzzy topological digital space concepts with their properties such as neighbourhood, interior and closure by using fuzzy set digital concept and Chang’s fuzzy topology approach. The product fuzzy topology digital also will be shown. By introduce this concept, the data in flat EEG can considering having fuzzy topology digital properties and can identify the area in fuzzy digital space that has been affected by epilepsy seizure in epileptic patient’s brain.

Topological Basis of Flat Electroencephalography’s State Space

Neuroinverse problem are often associated with complex neuronal activity. It involves locating problematic neuron which usually a highly challenging task. While epileptic foci localization is possible with the aid of EEG signals, it relies greatly on the ability to extract hidden information or pattern within EEG signals. Flat EEG being an enhancement of EEG is a way of viewing electroencephalograph on the real plane. In the perspective of dynamical systems, the equivalency of epileptic seizure and Flat EEG is important since the underlying structure of Flat EEG is dynamic. In this paper, a basis for the topological space of Flat EEG is presented. It is expected to facilitate in the constructions of dynamic transformation from epileptic seizure to Flat EEG. It provides a better understanding on Flat EEG itself.Â

Transcranial magnetic therapy is an effective strategy for remediating neuroendocrine pathology

Aspects of reactivation and remediation of impaired functions of the brain and of the inner organs regulatory systems are crucial to medical science. The study presents the technique of transcranial magnetic therapy (TMT) with extremely low frequency alternating magnetic field employed for balanced activation of central nervous system function. This study was aimed to assess the effectiveness of TMT in diseases caused by hypothalamic–pituitary dysfunction. Material and Methods ― 90 children aged 10-16 years with different diseases but with similar pathogenic patterns were enrolled in the study. Group 1 included 30 adolescent girls with menstrual irregularities. Group 2 included 30 children with nocturnal enuresis. Group 3 included 30 teenage boys with constitutional delay of growth and puberty. Medical histories were studied, clinical and laboratory evaluation was carried out. TMT stimulation was performed using the device “AMO-ATOS” (TRIMA LLC, Saratov, Russia). Results ― Children in all the groups had high incidence of antenatal and perinatal pathologies recorded in their medical histories. Analysis of electroencephalograms (EEG) showed the prevalence of disorganized and flat EEG patterns – 70% in all the children. Sympathicotonia being the symptom of autonomic nervous system dysfunction, prevailed in 60-80% of the children. The children in the three groups had hormonal imbalance. The treatment with TMT resulted in considerable improvement in hormonal balance and laboratory findings. Conclusion ― ТМТ stimulation is effective in remediation of impaired functions of the brain and treatment of the diseases caused by hypothalamic–pituitary dysfunction.

Status epilepticus: Refractory and super-refractory.

Status epilepticus (SE) is an important neurological emergency. It is defined as seizures lasting for 5 minutes or more or recurrent seizures without recovery of consciousness to baseline between the attacks. Refractory SE (RSE) is defined as SE persisting despite sufficient dose of benzodiazepines and at least one antiepileptic drug (AED), irrespective of time. Super refractory SE (SRSE) is defined as SE that continues for 24 hours or more after the use of anesthetic therapy, including cases that recur on weaning of the anesthestic agent. RSE occurs in 23%-48% of the patients and SRSE in approximately 22% of the patients with SE. In general, RSE occurs in patients with new-onset seizures rather than in patients with chronic epilepsy. The etiology of RSE in developing countries is dominated by central nervous system (CNS) infections and head injury compared to stroke and drug withdrawal in the developed countries. The treatment of RSE and SRSE is not evidence based. Following benzodiazepines, the second line antiepileptic drugs include sodium valproate, phenytoin, levetiracetam, and anesthetic drugs such as midazolam, phenobarbital, and propofol. Most intravenous anesthetic drugs produce hypotension and respiratory suppression; therefore, patients with RSE are managed in intensive care units (ICUs). In RSE patients, electroencephalogram (EEG) burst suppression with interburst interval of 2-20 s or even flat EEG has been tried. Recently, concerns have been raised on the safety of burst suppression in RSE and SRSE. The paucity of ICUs in developing countries limits the use of these management protocols. There is a need to explore intravenous AEDs with safer cardiovascular and respiratory profile for the management of SE.

Spatial Interaction Image of Electroencephalography Signal during Epileptic Seizure on Flat Electroencephalography

Electroencephalogram (EEG) is widely used for recording the brain electrical activity. It plays an important role in the detection and classification of epilepsy seizures. In addition, it is believed to possess the ability to detect the location of epileptic focus to some degree. In this study, an EEG data during seizure obtained is analysed and transformed into an image form, namely Flat EEG (fEEG). The fEEG is a method for mapping high dimensional signal into a low dimensional space. The fEEG of the obtained signal is clustered and the interactions of the cluster centers are studied and presented in this study.

THE DETERMINATION OF HESITATION VALUE FOR SUGENO TYPE INTUITIONISTIC FUZZY GENERATOR VIA FUZZY LIMIT

In intuitionistic fuzzy set (IFS) theory, the membership and non- membership values are needed to model the uncertainties. One of the ways to compute the non-membership is by implementing the Sugeno type intuitionistic fuzzy generator. In this paper, fuzzy limit will be used to determine the value of parameter, namely in the non-membership function. Different values of ? will directly control the level of hesitation, namely p, in the output image. Hence, the output images of Flat EEG (fEEG) during epileptic seizures are enhanced by using the window based enhancement scheme.

What does burst suppression really mean?

This paper describes the various electroencephalographic (EEG) patterns expressed by the comatose brain, starting with the sleep-like oscillations associated with light coma. Deeper coma generally displays a burst-suppression pattern characterized by alternating episodes of isoelectric (flat) EEG and bursting slow waves. The latter are the result of cortical hyperexcitability, as demonstrated by intracellular recordings in anesthetized animals. Further deepening of the coma yields to continuous isoelectric electroencephalogram and eventually results in a newly discovered type of spiky waves that have been termed Nu-complexes. The paper discusses the structures participating in the genesis of burst suppression, the afferent mechanisms, and the reasons for which this activity should or should not be regarded as an epileptic disorder. This article is part of a Special Issue entitled "Status Epilepticus".

Dynamical System of Flat Electroencephalography

Flat EEG is a way of viewing electroencephalography signals on the real plane. The wealth of information contained within makes it a great platform to study epileptic seizure. In this study, the time embedment nature of Flat EEG is exploited to establish a dynamic Flat EEG model. Basically, a geometrical representation which displays the important information visually for Flat EEG at any time will firstly be formulated, followed by the representation of a Flat EEG signal as a trajectory in state space. Based on these, the modeling of Flat EEG as a dynamical system using the notion of flow is presented. This dynamic model would serve as the groundwork for the indisputably essential dynamic justification of Flat EEG in future research.

Letters to the Editor: Is it Rational to Extrapolate from the Presence of Consciousness During a Flat EEG to Survival of Consciousness After Death

A letter from Titus Rivas to the editor of the Journal of Near-Death Studies on the topic: "Is it Rational to Extrapolate from the Presence of Consciousness During a Flat EEG to Survival of Consciousness After Death?"

Letters to the Editor: Response to "Is it Rational to Extrapolate from the Presence of Consciousness during a Flat EEG to Survival of Consciousness After Death?"

A letter from Janice Miner Holden to the editor of the Journal of Near-Death Studies on the topic: "Response to 'Is it Rational to Extrapolate from the Presence of Consciousness during a Flat EEG to Survival of Consciousness After Death?'"

Spatial interaction of electroencephalography signal during epileptic seizure on flat electroencephalography

Electroencephalography (EEG) is one of the fields in clinical neurophysiology, which is a recording of the electrical activity of the brain from the scalp. One of the major roles of EEG is as an aid to diagnose epilepsy. Abnormal patterns such as spikes, sharp waves and, spikes and wave complexes can be seen. It is important to extract spatial information about the dynamics from a few observations of this recorded signal regardless where EEG sensors are located. A developed method by Theoretical &amp; Computational Modelling for Complex System (TCM), UTM, namely Flat EEG for mapping high dimensional signal into a low dimensional space will be used as platforms to analyse EEG signal spatially during epileptic seizure. The spatial interactions among clusters are identified through spatial interaction model, namely gravity model. This paper also reveals that gravity model used is a measure.

Neuronal networks in Burst Suppression EEG as revealed by source analysis

Introduction: Burst-suppression (BS) is an electroencephalography (EEG) pattern consisting of alternative periods of slow waves of high amplitude (burst) and periods of so called flat EEG (suppression) (Swank & Watson, 1949). It is generally associated with comatose states of various etiologies (hypoxia, drug-related intoxication, hypothermia, and childhood encephalopathies, but also anesthesia). Animal studies suggest that both the cortex and the thalamus are involved in the generation of BS (Steriade et al., 1994).

Human Brain Activity Patterns beyond the Isoelectric Line of Extreme Deep Coma

The electroencephalogram (EEG) reflects brain electrical activity. A flat (isoelectric) EEG, which is usually recorded during very deep coma, is considered to be a turning point between a living brain and a deceased brain. Therefore the isoelectric EEG constitutes, together with evidence of irreversible structural brain damage, one of the criteria for the assessment of brain death. In this study we use EEG recordings for humans on the one hand, and on the other hand double simultaneous intracellular recordings in the cortex and hippocampus, combined with EEG, in cats. They serve to demonstrate that a novel brain phenomenon is observable in both humans and animals during coma that is deeper than the one reflected by the isoelectric EEG, and that this state is characterized by brain activity generated within the hippocampal formation. This new state was induced either by medication applied to postanoxic coma (in human) or by application of high doses of anesthesia (isoflurane in animals) leading to an EEG activity of quasi-rhythmic sharp waves which henceforth we propose to call ν-complexes (Nu-complexes). Using simultaneous intracellular recordings in vivo in the cortex and hippocampus (especially in the CA3 region) we demonstrate that ν-complexes arise in the hippocampus and are subsequently transmitted to the cortex. The genesis of a hippocampal ν-complex depends upon another hippocampal activity, known as ripple activity, which is not overtly detectable at the cortical level. Based on our observations, we propose a scenario of how self-oscillations in hippocampal neurons can lead to a whole brain phenomenon during coma.