Effect of Acquaintance on Inter-Brain EEG Synchronization
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The present study was conducted to detect the sleep stages by electroencephalography (EEG) using chaotic features. The method used in this study was the content analysis method. First, the sleep stages and EEG have been analyzed, and the EEG with chaotic features was used to detect the sleep stages. Detection of artifacts in sleep electroencephalography (EEG) is one of the vital tasks in the pre-processing stage. Despite many artifact exploration algorithms over the years, lots of them lose their advantages to use sleep EEG. Types of brain activities can be measured, and the involved brain areas can be detected using EEG. Electroencephalography (EEG) signal includes different rhythms, which are dependent on various sensory and movement conditions. Detection of each rhythm of this signal needs experience and skills. As a result, analysis of the signal recorded by EEG can be used widely for detection and academic purposes.
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Limited access to specialized technicians and trained neurologists results in delayed access to electroencephalography (EEG) and an accurate diagnosis of patients with critical neurological problems. This study evaluated the performance of Ceribell Rapid Response EEG System (RR-EEG), which promises fast EEG acquisition and interpretation without traditional technicians or EEG-trained specialists.The new technology was tested in a community hospital intensive care unit in Northern California. Three physicians (without previous training in EEG) were trained by the manufacturer of the RR-EEG and acquired EEG without the help of any EEG technicians. Time needed from order to EEG acquisition was noted. Quality of EEG and diagnostic information obtained with the new EEG technology were evaluated and compared with the same information from conventional clinical EEG system.Ten patients were tested with this new EEG technology, and 6 of these patients went on to have conventional EEGs when the EEG technicians arrived at the site. In these cases, the conventional EEG was significantly delayed (11.2 ± 3.6 hours) compared with RR-EEG (5.0 ± 2.4 minutes; P < .005). Use of RR-EEG helped clinicians rule out status epilepticus and prevent overtreatment in 4 of 10 cases. RR-EEG and conventional EEG systems yielded similar diagnostic information.RR-EEG can be set up by nurses, and diagnostic information about the presence or absence of seizures can be appreciated by nurses. The RR-EEG system, compared with the conventional EEG, did not require EEG technologists and enabled significantly faster access to diagnostic EEG information. This report confirms the ease of use and speed of acquisition and interpretation of EEG information at a community hospital setting using an RR-EEG device. This new technology has the potential to improve emergent clinical decision making and prevent overtreatment of patients in the intensive care unit setting while empowering nursing staff with useful diagnostic information in real time and at the bedside.
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Introduction: The aim of this study was to compare routine awake electroencephalography (r-EEG), melatonin-induced sleep EEG (m-EEG) and EEG (d-EEG) after sleep deprivation studies in terms of epileptiform anomalies (EA), and to compare d-EEG and m-EEG studies in terms of sleep induction in patients requiring differential diagnosis of epileptic seizure/nonepileptic seizure. Methods: The study included 45 patients aged 18–45 years who had at least one seizure suspected to be epileptic but could not be diagnosed with epilepsy with clinical and laboratory findings. Each patient underwent r-EEG on the 1 st day, d-EEG on the 2 nd day after 24 h of sleeplessness, and m-EEG on the 3 rd day after the administration of 6 mg melatonin following 7 h night sleep. Three separate EEG tracings of the patients were compared for EA. The d-EEG and m-EEG methods were examined for their ability to achieve sleep, total sleep time (ST), and sleep latency (SL). Results: When the detection rate of EA in d-EEG and m-EEG was compared with that of r-EEG, it was found to be significantly higher ( P < 0.001) (73.3% with d-EEG, 75.6% with m-EEG, and 35.6% with r-EEG). Sleep was achieved at a rate of 100% after receiving melatonin and at a rate of 97.8% with sleep deprivation. There was no significant difference between d-EEG and m-EEG in terms of mean ST and SL (ST = 58.6 ± 12.6 min and 59.7 ± 8.3 min, respectively; SL = 287.6 ± 484.3 s and 152.2 ± 178.7 s after the start of the EEG, respectively). Conclusions: Sleep EEG is superior to awake EEG in terms of detecting EA. In an EEG study, where melatonin was used to induce sleep, the sleep rate and SL were similar to those of d-EEG, and melatonin did not have an EA increasing or suppressing effect on EEG. Given the ease of application and low side effect profile, it is thought that m-EEG may be an applicable method in the diagnosis of epilepsy.
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Electroencephalography (EEG) is an essential investigative tool for use in young people with epilepsy. This study assesses the effects of different EEG protocols on the yield of EEG abnormalities in young people with possible new epilepsy.85 patients presenting to the unit underwent three EEGs with differing protocols: routine EEG (r-EEG), sleep-deprived EEG (SD-EEG), EEG carried out during drug-induced sleep (DI-EEG). The yield of EEG abnormalities was compared using each EEG protocol.98 patients were recruited to the study. Of the 85 patients who completed the study, 33 (39%) showed no discernible abnormality on any of their EEG recordings. 36 patients (43%) showed generalised spike and wave during at least one EEG recording, whereas 15 (18%) had a focal discharge evident at some stage. SD-EEG had a sensitivity of 92% among these patients, whereas the sensitivity of DI-EEG and r-EEG was 58% and 44%, respectively. The difference between the yield from SD-EEG was significantly higher than that from other protocols (p < 0.001). Among the 15 patients showing focal discharges, SD-EEG provoked abnormalities in 11 (73%). r-EEG and DI-EEG each produced abnormalities in 40% and 27%, respectively. 7 patients (47%) had changes seen only after sleep deprivation. In 2 (13%), the only abnormalities were seen on r-EEG. In only 1 patient with focal discharges (7%) was the focal change noted solely after drug-induced sleep. These differences did not reach significance.EEG has an important role in the classification of epilepsies. SD-EEG is an easy and inexpensive way of increasing the yield of EEG abnormalities. Using this as the preferred protocol may help reduce the numbers of EEGs carried out in young patients presenting with epilepsy.
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To compare the quality of electroencephalography (EEG) signals recorded with a rapid response EEG system and the signals recorded with conventional clinical EEG recordings.We studied the differences between EEG recordings taken with a rapid response EEG system (Ceribell) compared to conventional EEG through two separate set of studies. First, we conducted simultaneous recording on a healthy subject in an experimental laboratory setting where the rapid response EEG and two conventional EEG recording systems (Nihon Kohden and Natus) were used at the same time on the same subject using separate but adjacently placed electrodes. The rapid response EEG was applied by a user without prior training in EEG set up while two separate sets of conventional EEG electrodes were placed by a trained EEG technologist. The correlation between each of the recordings was calculated and quantitatively compared. In the second study, we performed a set of consecutive recordings on 22 patients in an ICU environment. The rapid response EEG system was applied by clinical ICU fellows without prior training in EEG set up while waiting for the conventional EEG system to arrive, after which the rapid response EEG was stopped and the conventional EEG was applied by a trained EEG technologist. We measured and compared several metrics of EEG quality using comparative metrics.For the simultaneous recording performed in a laboratory environment, the tested rapid response EEG and conventional EEG recordings showed agreement when aligned and visually compared in the time domain, all EEG waveform features were distinguishable in both recordings. The correlation between each pair of recordings also showed that the correlation between the rapid response EEG recording and each of the two conventional recordings was statistically the same as the correlation between the two conventional recordings. For the consecutive recordings performed in real life clinical ICU environment, Hjorth parameters, spike count, baseline wander, and kurtosis measures were statistically similar (p > 0.05, Wilcoxon signed rank test) for the rapid response EEG and conventional clinical EEG recordings. The rapid response EEG data had significantly lower 60 Hz noise compared to recordings made with the conventional systems both in laboratory and ICU settings. Lastly, the clinical information obtained with the rapid response EEG system was concordant with the diagnostic information obtained with the conventional EEG recordings in the ICU setting.Our findings show that the tested rapid response EEG system provides EEG recording quality that is equivalent to conventional EEG systems and even better when it comes to 60 Hz noise level. The concordance between the rapid response EEG and conventional EEG systems was demonstrated both in a controlled laboratory environment as well as in the noisy environment of a hospital ICU on patients with altered mental status.Our findings clearly confirm that the tested rapid response EEG system provides EEG data that is equivalent in quality to the recordings made using conventional EEG systems despite the fact that the rapid response system can be applied within few minutes and with no reliance on specialized technologists. This can be important for urgent situations where the use of conventional EEG systems is hindered by the lengthy setup time and limited availability of EEG technologists.
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Burst-suppression EEG (BS-EEG) after cardiopulmonary resuscitation implies a bad prognosis, but little is known of the temporal dynamics of postanoxic BS-EEG. The authors studied 24 consecutive patients who developed BS-EEG within 24 hours after cardiopulmonary resuscitation, and followed 20 of these patients with serial EEGs. Except for one patient, BS-EEG was followed by another EEG pattern within 1 day, mainly areactive alpha EEG (n = 6), isoelectric EEG (n = 5), generalized continuous epileptiform discharges (n = 4), or theta; EEG (n = 3). The coexistence of different EEG patterns in the same recording was seen in 10 patients. Serial recordings disclosed a variety of EEG sequences with (often subtle) transitions between the different EEG patterns, including reappearance of BS-EEG. Postanoxic BS-EEG is followed by a variety of EEG sequences composed of different EEG patterns, each of which is recognized as an unfavorable sign in and of itself. The coexistence of different unfavorable EEG patterns in the same recording, and transitions between these EEG patterns in subsequent recordings, are common in patients with postanoxic BS-EEG. It seems reasonable to speculate that BS-EEG and subsequently evolving EEG patterns in anoxic encephalopathy reflect different forms of neocortical dysfunction, which occur at different stages of a dynamic process, leading ultimately to severe neuronal loss.
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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The nationally-recognized Susquehanna
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