Electroencephalogram Monitoring Research Articles

End-to-end model for automatic seizure detection using supervised contrastive learning

Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures, caused by abnormal electrical activity in cerebral neurons. Given that it is one of the most common neurological disorders globally, the efficient and accurate automatic seizure detection is urgently needed in the diagnosis of epilepsy to reduce the workload of continuous electroencephalogram (EEG) monitoring. Current deep learning based seizure detection approaches usually employ cross-entropy loss as objective function, which generally suffer from inadequate utilization of sample labels and poor classification margins, resulting in decreased performance in seizure detection. In this study, we propose an end-to-end automatic seizure detection framework based on supervised contrastive learning, which effectively utilizes labeled EEG to cluster similar samples while separating dissimilar ones. A supervised contrastive learning loss is employed to optimize classification boundaries by making full use of EEG labels. We employ long-term continuous EEG for evaluation. Given the presence of various noise and interferences, assessment on long-term continuous EEG proves to be more challenging. Post-processing techniques such as smoothing filter, threshold judgment, and collar technique are further adopted to diminish the artifact impacts on seizure detection performance. The proposed method is evaluated on the publicly available Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, achieving an event-based sensitivity of 99.71% and a false detection rate (FDR) of 0.35/h.

Paroxysmal sympathetic hyperexcitability after brain injury: A clinical analysis of case series.

Paroxysmal sympathetic hyperexcitability (PSH) is a group of complex syndromes with various etiologies. Previous studies were limited to the description of traumatic brain injury (TBI), and the description of PSH after other types of brain injury was rare. We explored the clinical features, treatment, and prognosis of PSH after various types of brain injuries. Patients admitted to the neurosurgery intensive care unit with PSH after brain injury from July 2019 to December 2022 were included. Demographic data, clinical manifestations, drug therapy, and disease prognosis were retrospectively collected and analyzed. Fifteen male and 9 female patients with PSH after brain injury were selected. TBI was most likely to cause PSH (66.7%), followed by spontaneous intracerebral hemorrhage (25%). Glasgow coma scale scores of 19 patients (79.2%) were lower than 8 and 14 patients (58.3%) underwent tracheotomy. Electroencephalogram monitoring was performed in 12 individuals, none of which showed epileptic waves. Clinical symptom scale showed mild symptoms in 17 cases (70.8%). Almost all patients were administered a combination of drugs. After follow-up, most patients had a poor prognosis and 2 (8.3%) died after discharge. The etiology of PSH is complex. TBI may be the most common cause of PSH. Non-TBI may also be an important cause of PSH. Therefore, early identification, prevention and diagnosis are helpful for determining the prognosis and outcome of the disease.

What are standard monitoring devices for anesthesia in future?

Monitoring the patient's physiological functions is critical in clinical anesthesia. The latest version of the Japanese Society of Anesthesiologists' Guidelines for Safe Anesthesia Monitoring, revised in 2019, covers various factors, including electroencephalogram monitoring, oxygenation, ventilation, circulation, and muscle relaxation. However, with recent advances in monitoring technologies, the information provided has become more detailed, requiring practitioners to update their knowledge. At a symposium organized by the Journal of Anesthesia in 2023, experts across five fields discussed their respective topics: anesthesiologists need to interpret not only the values displayed on processed electroencephalogram monitors but also raw electroencephalogram data in the foreseeable future. In addition to the traditional concern of preventing hypoxemia, monitoring for potential hyperoxemia and the effects of mechanical ventilation itself will become increasingly important. The importance of using AI analytics to predict hypotension, assess nociception, and evaluate microcirculation may increase. With the recent increase in the availability of neuromuscular monitoring devices in Japan, it is important for anesthesiologists to become thoroughly familiar with the features of each device to ensure its effective use. There is a growing desire to develop and introduce a well-organized, integrated "single screen" monitor.

Integrating rSO2 and EEG monitoring in cardiopulmonary resuscitation: A novel methodology

Despite improvements in cardiopulmonary resuscitation (CPR), survival and neurologic recovery after cardiac arrest remain poor due to ischemia and subsequent reperfusion injury. As the likelihood of survival and favorable neurologic outcome decreases with increasing severity of ischemia during CPR, developing methods to measure the magnitude of ischemia during resuscitation is critical for improving overall outcomes. Cerebral oximetry, which measures regional cerebral oxygen saturation (rSO2) by near‐infrared spectroscopy, has emerged as a potentially beneficial marker of cerebral ischemia during CPR. In numerous preclinical and clinical studies, higher rSO2during CPR has been associated with improved cardiac arrest survival and neurologic outcome. There is also emerging evidence that this can be integrated with electroencephalogram (EEG) monitoring to provide a bimodal system of brain monitoring during CPR. In this method’s review, we discuss the feasibility, application, and implications of this integrated monitoring approach, highlighting its significance for improving clinical outcomes in cardiac arrest management and guiding future research directions.

Analyzing the States of Brain under Anesthesia Using ANES-metanet based on Deep Learning Models

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time .Continuous monitoring of electroencephalogram (EEG) recordings in humans under general anesthesia (GA), local anesthesia (LA) and regional anesthesia (RA) has demonstrated that changes in EEG dynamics induced by an anesthetic drug are reliably associated with the altered arousal states caused by the drug. This observation suggests that an intelligent, closed-loop anesthesia delivery (CLAD) system operating in real-time could track EEG dynamics and control the infusion rate of a programmable pump to precisely maintain unconsciousness. In this process we propose a deep learning algorithm to analysis the anesthesia from EEG signal. So the deep learning algorithms are Convolution Neural Network, Long Short Term Neural Network and Artificial Neural Network will applied and analyze EEG signal and it generates the results in the form of metrics like accuracy, precision, recall and f1-score.

Changes in the Objective Measures of Sleep in Association with Menses Among Female Athletes with Poor Subjective Sleep Quality: Female Athletes with Poor Subjective Sleep Quality Have More Sleep Arousals During Menses.

Female athletes with menstrual abnormalities have poor sleep quality. However, whether female athletes with poor sleep quality based on subjective assessment have distinctive changes in objective measures of sleep in association with menses remains unclear. This study aimed to compare changes in objective sleep measurements during and following menses between collegiate female athletes with and without poor subjective sleep quality. Female collegiate athletes (age range/mean ± standard deviation: 18-22/ 22.2±1.1) with regular menstrual cycles were recruited. The participants underwent home electroencephalogram monitoring during the first and second nights after the onset of menses and one night between the seventh and 10th nights after menses onset (mid-follicular phase). The Pittsburgh Sleep Quality Index (PSQI) was used to assess the subjective sleep quality. Interactions between the presence of poor subjective sleep quality (ie, PSQI ≥6) and changes in objective measures of sleep in association with menses were analyzed. Data of 45 athletes, including 13 with poor subjective sleep quality, showed that changes in arousal index in athletes with poor subjective sleep quality were distinctive from those in athletes without poor subjective sleep quality (p = 0.036 for interaction). In athletes with poor subjective sleep quality, the arousal index was significantly increased in menses (p for analysis of variance, 0.015), especially on the first night after the onset of menses compared with during the mid-follicular phase (p = 0.016). Collegiate female athletes with regular menstrual cycles are likely to have poor subjective sleep quality in association with more frequent arousal during the first night after the onset of menses than during the mid-follicular phase.

Cerebral malaria presenting as nonconvulsive status epilepticus: a case report

BackgroundMalaria is an infectious malady caused by Plasmodium parasites, cerebral malaria standing out as one of its most severe complications. Clinical manifestation include elevated body temperature, loss of consciousness, and seizures. However, reports of cerebral malaria presenting as nonconvulsive status epilepticus are extremely rare. The case presented involves psychiatric symptoms, with the electroencephalogram indicated nonconvulsive status epilepticus associated with cerebral malaria.Case presentationA 53-year-old male, was urgently admitted, due to confusion and abnormal behaviour for 10 h. The patient returned to China after developing a fever while working in Tanzania two months ago. The blood smear revealed Plasmodium vivax and Plasmodium falciparum, and he was diagnosed with malaria. He recovered following anti-malarial treatment. After admission, the patient was confused, unable to communicate normally, and unwilling to cooperate with the physical examination. Plasmodium was not found in the blood smear, but the DNA sequence of P. falciparum was discovered using metagenomic next-generation sequencing of cerebrospinal fluid. Brain MRI revealed no significant abnormalities. Continuous electroencephalogram monitoring revealed that the patient had non-convulsive status epilepticus, which was treated with diazepam and levetiracetam. The patient had normal consciousness and behaviour. He received anti-malarial treatment for two weeks and fully recovered.ConclusionsThis case demonstrates that nonconvulsive status epilepticus can be a manifestation of cerebral malaria. It is imperative for attending physicians to heighten vigilance when encountering patients with a history of travel to malaria-endemic regions or a prior malaria infection, especially in the presence of unusual clinical presentations.

D‐Unet: A symmetric architecture of convolutional neural network with two auxiliary outputs for dementia recognition

AbstractDementia‐associated disorders cause damage to the brains of patients and bring huge burdens to individuals and families. Electroencephalogram (EEG) monitoring is friendly to patients on account of low cost, non‐invasion, and objective. Event‐related potential (ERP) is a component of EEG that has huge potential to evaluate the cognitive function of the brain. In this study, we recorded the ERP from patients with dementia and healthy people, then proposed an ERP‐based deep learning method to realize dementia recognition via the model Dementia‐Unet (D‐Unet). To improve the performance of the model, on the base of the decoder and the primary classifier, we added new structures including a symmetric decoder and two auxiliary outputs. One of the auxiliary outputs was input reconstruction, and the other one was aimed at working like the primary classifier with the same task. The results of the experiment of four‐fold cross‐validation demonstrated the two auxiliary outputs improved the performance of the model effectively. When compared with some other machine learning methods and deep learning methods, our model obtained the best performance with an accuracy of 0.815, a precision of 0.829, a recall of 0.797, and an f1‐score of 0.812. Besides, we put up a complex training strategy with all outputs involved, but a simple testing strategy with only a primary classifier working to keep high performance but cut down the complexity burden during testing.

The diagnostic yield of a 2-h versus a 30-min electroencephalogram for patients with altered mental status in neurological intensive care unit

BackgroundThe continuous electroencephalogram (cEEG) monitoring is recommended for critical ill patients with altered mental status, The cEEG detects seizure activity and nonconvulsive status epilepticus (NCSE) more efficiently than routine electroencephalogram (rEEG). Yet, cEEG is not widely available in many neurological intensive care units (NICU) in limited resource countries. Ideal study duration, and whether cEEG is useful and cost-effective in terms of patient outcome compared to (rEEG), is still questions under research. The main objective in our study was the relative diagnostic yield of the 2 h, prolonged EEG recording, compared to the 30-min rEEG recording in the NICU.ResultsThere was no significant diagnostic yield for the 2-h electroencephalogram (EEG) over the 30-min EEG for patient with altered mental status in the NICU. Being with altered mental state in an NICU carries a poor outcome, we found that death occurred in 68.9% of patients. Acute ischemic stroke is the most common cause of altered mental status in NICU by about 53.3% of patients. Less than half of the patients (42.2%) were receiving anti-seizure medications (ASM).ConclusionsAs long as the cEEG is not available in the NICU, the 30-min EEG has a good diagnostic yield and it is almost equivalent to the prolonged 2-h EEG, and if the EEG is recommended for a longer duration, we recommend a long-term EEG more than 2 h.

Relationship of Neonatal Seizure Burden Before Treatment and Response to Initial Antiseizure Medication

To assess among a cohort of neonates with hypoxic-ischemic encephalopathy (HIE) the association of pretreatment maximal hourly seizure burden and total seizure duration with successful response to initial antiseizure medication (ASM). This was a retrospective review of data collected from infants enrolled in the HEAL Trial (NCT02811263) between January 25, 2017, and October 9, 2019. We evaluated a cohort of neonates born at ≥36weeks of gestation with moderate-to-severe HIE who underwent continuous electroencephalogram monitoring and had acute symptomatic seizures. Poisson regression analyzed associations between (1) pretreatment maximal hourly seizure burden, (2) pretreatment total seizure duration, (3) time from first seizure to initial ASM, and (4) successful response to initial ASM. Among 39 neonates meeting inclusion criteria, greater pretreatment maximal hourly seizure burden was associated with lower chance of successful response to initial ASM (adjusted relative risk for each 5-minute increase in seizure burden 0.83, 95% CI 0.69-0.99). There was no association between pretreatment total seizure duration and chance of successful response. Shorter time-to-treatment was paradoxically associated with lower chance of successful response to treatment, although this difference was small in magnitude (relative risk 1.007, 95% CI 1.003-1.010). Maximal seizure burden may be more important than other, more commonly used measures in predicting response to acute seizure treatments.

Risk Factors for In-Hospital Seizure and New-Onset Epilepsy in Coiling and Clipping Treatment of Aneurysmal Subarachnoid Hemorrhage

This study aimed to identify the risk factors associated with in-hospital seizures and new-onset epilepsy in patients with aneurysmal subarachnoid hemorrhage who underwent coiling embolization or clipping surgery. A retrospective descriptive study was conducted on 195 patients diagnosed with aneurysmal subarachnoid hemorrhage and treated with coiling embolization or clipping surgery between January 2018 and June 2022. Among the 195 patients meeting the inclusion criteria, 9 cases experienced an onset seizure at the time of subarachnoid hemorrhage. In-hospital seizures were observed in 33 cases, in which electrographic seizures were detected in 24 patients with suspected subclinical seizures. After 12 months of follow-up, 11 patients met the criteria for an epilepsy diagnosis. The incidence of epilepsy after discharge at 12 months was 2.41% in the coiling group and 8.03% in the clipping group. The risk of in-hospital seizures was significantly higher in the clipping group (p = 0.007), although the difference was not statistically significant after 12 months of follow-up (p = 0.121). The occurrence of epilepsy following aneurysmal subarachnoid hemorrhage was relatively common. Clipping surgery and brain edema emerged as independent predictive factors for in-hospital seizures, while onset seizures and in-hospital seizures were identified as independent predictors of epilepsy during follow-up. Patients presenting these risk factors may benefit from long-term electroencephalogram monitoring and should be considered for prophylactic antiepileptic drugs. Additionally, lumbar drainage proved effective in improving both early and late epileptic outcomes in the Fisher III-IV group.

Seizure recurrences in generalized convulsive status epilepticus under sedation: What are its predictors and its impact on outcome?

BackgroundManagement of status epilepticus (SE) is focused on the early seizure termination. Refractory SE is an indication for sedation in patients with SE, but up to 75% of patients may be ventilated due to a neurological or respiratory failure. In patients requiring sedation, the clinical assessment is not sufficient to assess seizure control. Identifying those at risk of recurrent seizures could be useful to adapt their management. On the other hand, patients with low risk could benefit from an early withdrawal of sedation to avoid the impact of inappropriate sedation on outcome. ObjectiveTo determine the prevalence and the predictors of uncontrolled SE and its impact on outcome in patients with generalized convulsive SE (GCSE) requiring mechanical ventilation (MV). MethodsWe retrospectively included patients admitted to the intensive care unit with GCSE requiring MV. Uncontrolled SE was defined as persistent or recurrent seizures during sedation or within 24hours following withdrawal. A multivariable logistic regression model was used to assess the associated factors. ResultsUncontrolled SE occurred in 37 out of 220 patients (17%). Persistent seizures at admission, higher SAPS II and central nervous system infection were associated with a higher risk of uncontrolled SE. Acute toxic or metabolic etiologies were associated with a decreased risk of uncontrolled SE. In a supplementary analysis, decrease of albumin blood levels was associated with uncontrolled SE. Uncontrolled SE was associated with a poor functional outcome and mortality at 90 days. ConclusionsSeventeen percent of patients with a GCSE requiring MV suffered from uncontrolled SE. Etiology and persistent seizures at admission were the main predictors of uncontrolled SE. Patients with uncontrolled SE had a longer duration of sedation and MV, a poor functional outcome and a higher mortality. Further studies are required to determine the impact of continuous electroencephalogram monitoring on the clinical course.

Identifying inpatient hospitalizations with continuous electroencephalogram monitoring from administrative data

BackgroundContinuous electroencephalography (cEEG) is increasingly utilized in hospitalized patients to detect and treat seizures. Epidemiologic and observational studies using administrative datasets can provide insights into the comparative and cost effectiveness of cEEG utilization. Defining patient cohorts that underwent acute inpatient cEEG from administrative datasets is limited by the lack of validated codes differentiating elective epilepsy monitoring unit (EMU) admissions from acute inpatient hospitalization with cEEG utilization. Our aim was to develop hospital administrative data-based models to identify acute inpatient admissions with cEEG monitoring and distinguish them from EMU admissions.MethodsThis was a single center retrospective cohort study of adult (≥ 18 years old) inpatient admissions with a cEEG procedure (EMU or acute inpatient) between January 2016-April 2022. The gold standard for acute inpatient cEEG vs. EMU was obtained from the local EEG recording platform. An extreme gradient boosting model was trained to classify admissions as acute inpatient cEEG vs. EMU using administrative data including demographics, diagnostic and procedure codes, and medications.ResultsThere were 9,523 patients in our cohort with 10,783 hospital admissions (8.5% EMU, 91.5% acute inpatient cEEG); with average age of 59 (SD 18.2) years; 46.2% were female. The model achieved an area under the receiver operating curve of 0.92 (95% CI [0.91–0.94]) and area under the precision-recall curve of 0.99 [0.98–0.99] for classification of acute inpatient cEEG.ConclusionsOur model has the potential to identify cEEG monitoring admissions in larger cohorts and can serve as a tool to enable large-scale, administrative data-based studies of EEG utilization.

Initial Management of Status Epilepticus

Status epilepticus(SE)is defined as a prolonged seizure and is a common neurological emergency with high morbidity and mortality rates. As uncontrolled SE causes irreversible neurological damage, prompt diagnosis and treatment are required. If anti-seizure medications and benzodiazepines, which are initial treatments for SE, are not effective and SE deteriorates to refractory, anesthetic drugs are needed to suppress seizure activity under electroencephalogram(EEG)monitoring. Continuous EEG monitoring is useful not only for evaluating the control of SE but also for diagnosing non-convulsive SE(NCSE)and psychogenic non-epileptic seizures. New-onset refractory status epilepticus is defined as refractory SE in a patient without active epilepsy and without a clear acute or active structural, toxic, or metabolic cause. Because autoimmune encephalitis is the most frequently identified cause, immunotherapy can be attempted in addition to antiepileptic treatment within 2 weeks. Although NCSE is the major cause of unconsciousness, diagnosis is difficult because of uncertain clinical symptoms. Continuous EEG monitoring over 24 h is crucial for diagnosis, although arterial spin labeling-magnetic resonance imaging is alternatively useful. Finally, the building of a multidisciplinary cooperation system is required for prompt diagnosis and intensive treatment for controlling SE.

Can perioperative electroencephalogram and adverse hemodynamic events predict neurodevelopmental outcomes in infants with congenital heart disease?

ObjectiveThe study objective was to characterize preoperative and postoperative continuous electroencephalogram metrics and hemodynamic adverse events as predictors of neurodevelopment in congenital heart disease infants undergoing cardiac surgery. MethodsFrom 2010 to 2021, 320 infants underwent congenital heart disease surgery at our institution, of whom 217 had perioperative continuous electroencephalogram monitoring and were included in our study. Neurodevelopment was assessed in 76 patients by the Bayley Scales of Infant and Toddler Development, 3rd edition, consisting of cognitive, communication, and motor scaled scores. Patient and procedural factors, including hemodynamic adverse events, were included by means of the likelihood of covariate selection in our predictive model. Median (25th, 75th percentile) follow-up was 1.03 (0.09, 3.44) years with 3 (1, 6) Bayley Scales of Infant and Toddler Development, 3rd Edition evaluations per patient. ResultsMedian age at index surgery was 7 (4, 23) days, and 81 (37%) were female. Epileptiform discharges, encephalopathy, and abnormality (lethargy and coma) were more prevalent on postoperative continuous electroencephalograms, compared with preoperative continuous electroencephalograms (P < .005). In 76 patients with Bayley Scales of Infant and Toddler Development, 3rd edition evaluations, patients with diffuse abnormality (P = .009), waveform discontinuity (P = .007), and lack of continuity (P = .037) on preoperative continuous electroencephalogram had lower cognitive scores. Patients with synchrony (P < .005) on preoperative and waveform continuity (P = .009) on postoperative continuous electroencephalogram had higher fine motor scores. Patients with postoperative adverse events had lower cognitive (P < .005) and gross motor scores (P < .005). ConclusionsPhenotypic patterns of perioperative continuous electroencephalogram metrics are associated with late-term neurologic injury in infants with congenital heart disease requiring surgery. Continuous electroencephalogram metrics can be integrated with hemodynamic adverse events in a predictive algorithm for neurologic impairment.

Automatic Epilepsy Detection using Machine Learning

Epilepsy is a brain disorder which causes seizures. Nerve cell activity of brain is disturbed which repeated seizures and disrupts the normal life of human. The nerve cells are connected with each other in complex way to communicate with each other. Epilepsy is neurological disorder where these nerve cells function in different way. This can be evaluated with help of Electroencephalogram (EEG) and Electrocorticography (ECoG) monitoring. Electroencephalogram help to check the signals of brain through images. These when monitored through machine learning system can help to evaluate whether the person has epilepsy disorder. The high-volume data is used to classify the disorder with machine learning classifier and statistical features The system is implemented with convolutional neural network for high dataset of image of signals of Electroencephalogram to classify the disorder. The research is ongoing where the performance is evaluated with different classifier and features.

MICROBIOTA&lt;b&gt; &lt;/b&gt;AMBIENTAL DEL DEPARTAMENTO DE NEONATOLOGIA EN EL INSTITUTO NACIONAL MATERNO PERINATAL, LIMA&lt;b&gt;, PERÚ&lt;/b&gt;

Introduction. Healthcare-associated infections (IAAS) increase neonatal morbidity and mortality. Identifying the microbiological profile and sensitivity of the microbiota allows us to reduce infection rates, rationalize the use of antibiotics and improve neonatal survival. Objectives. To know the environmental microbiota and antimicrobial sensitivity in the Department of Neonatology (DN). Methods. Observational, descriptive, cross-sectional study, carried out between April and May 2021, in the Intensive Care, Intermediate and Immediate Care services of the DN of the Instituto Nacional Materno Perinatal, a reference center for maternal and neonatal care. 144 surfaces were selected from the hospitalized patient environment. Using swabbing technique, samples were obtained and then seeded in common and selective culture media. Sensitivity was determined using Kirby-Bauer diffusion disks, the analysis of variables was performed with SPSS version 22.0 Results. Of 147 samples, 67% gave positive cultures with pathogenic bacteria. In immediate care 50% tested positive, in intermediate care 46.5% and in NICU 39.7%. The 100% contaminated surfaces were fan screens, electroencephalogram monitor, chart holders and scales. Of 41 hands of health personnel, 29.3% were contaminated: 54.5% nursing technicians, 30% doctors and 23% nursing graduates. The isolated germs were SCN (67%) sensitive to vancomycin, Enterococcus (16.5%) sensitive to ampicillin and vancomycin, Pseudomonas (8.2%) to meropenem, Escherichia coli (2.4%) to gentamicin and amikacin, Klebsiella (1.2%). Conclusion. Pathogens causing IAAS were isolated on surfaces in the environment of our patients. The bacterial agents found have high antimicrobial resistance and virulence.

"Drum Tower Hospital" strategy for acute type A aortic dissection with coma.

Acute type A aortic dissection (ATAAD) with persistent coma is a life-threatening condition associated with high mortality and poor neurological outcomes. The optimal timing for surgical intervention in these patients remains uncertain, and many patients are not eligible for surgery due to their poor prognosis. In this case, a 53-year-old man with hypertension presented to the emergency department in a coma that had lasted for 9hours. The patient was diagnosed with ATAAD and underwent the "Drum Tower Hospital" strategy, which involved preoperative assessments, including computed tomography angiography (CTA) and quantitative electroencephalogram (qEEG) monitoring. Surgical interventions, such as emergency stenting and aortic replacement, were performed to restore blood flow and repair the aorta. Postoperative monitoring, including qEEG, showed improvements in brain function. Despite the patient experiencing hemiplegia and a neurological deficit, the "Drum Tower Hospital" strategy, guided by comprehensive brain assessments, showed promise in managing ATAAD with coma. However, further research is needed to establish effective treatment strategies for these patients. Overall, ATAAD with persistent coma is a critical condition with limited treatment options. The "Drum Tower Hospital" strategy, supported by multimodal brain assessment, offers a potential approach to improve outcomes in these patients.

Efficacy and safety of remimazolam besilate for sedation in outpatients undergoing impacted third molar extraction: a prospective exploratory study

BackgroundDental treatments often cause anxiety, fear, and stress in patients. Intravenous sedation is widely used to alleviate these concerns, and various agents are employed for sedation. However, it is important to find safer and more effective sedation agents, considering the adverse effects associated with current agents. This study aimed to investigate the efficacy and safety of remimazolam besilate (hereinafter called “remimazolam”) and to determine the optimal dosages for sedation in outpatients undergoing dental procedures.MethodsThirty-one outpatients aged 18–65 years scheduled for impacted third molar extraction were included in the study. Remimazolam was administered as a single dose of 0.05 mg/kg followed by a continuous infusion at a rate of 0.35 mg/kg/h, with the infusion rate adjusted to maintain a sedation level at a Modified Observer’s Assessment of Alertness/Sedation (MOAA/S) score of 2–4. The primary endpoint was the sedation success rate with remimazolam monotherapy, and the secondary endpoints included induction time, recovery time, time until discharge, remimazolam dose, respiratory and circulatory dynamics, and frequency of adverse events.ResultsThe sedation success rate with remimazolam monotherapy was 100%. The remimazolam induction dose was 0.08 (0.07–0.09) mg/kg, and the anesthesia induction time was 3.2 (2.6–3.9) min. The mean infusion rate of remimazolam during the procedure was 0.40 (0.38–0.42) mg/kg/h. The time from the end of remimazolam administration to awakening was 8.0 (6.7–9.3) min, and the time from the end of remimazolam administration to discharge was 14.0 (12.5–15.5) min. There were no significant respiratory or circulatory effects requiring intervention during sedation.ConclusionsContinuous intravenous administration of remimazolam can achieve optimal sedation levels without significantly affecting respiratory or circulatory dynamics. The study also provided guidance on the appropriate dosage of remimazolam for achieving moderate sedation during dental procedures. Additionally, the study findings suggest that electroencephalogram monitoring can be a reliable indicator of the level of sedation during dental procedural sedation with remimazolam.Trial registrationThe study was registered in the Japan Registry of Clinical Trials (No. jRCTs061220052) on 30/08/2022.

Stimulus-induced periodic discharges in response to low-frequency photostimulation in a female patient in recovery period after middle cerebral artery stroke

Stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs) represent a relatively common phenomenon recorded during a long-term electroencephalogram (EEG) monitoring allowing to capture the altered state and response to auditory, tactile or nociceptive stimulation in critically ill patients. It is a nosologically non-specific phenomenon, and its relation to ictal event remains debated. We present a clinical case in which SIRPIDs were recorded in the affected dominant hemisphere in response to low-frequency photostimulation in a 60-years-old woman recovering after middle cerebral artery stroke. No ictal events were recorded during routine EEG monitoring; the patient was not in critical condition.