Continuous electroencephalography in a mixed non-neurological intensive care population, an observational study

Background: Seizures and status epilepticus are common neurologic complications in the intensive care unit (ICU) but the incidence in a cancer ICU is unknown. It is important to understand seizure risk factors in cancer patients to properly diagnose the seizure type to ensure appropriate therapy. Methods: We identified patients admitted to the medical ICU at Memorial Sloan Kettering Cancer Center (MSK) from January 2016 to December 2017 who had continuous or routine electroencephalography (EEG) and identified clinical and electrographic seizures by chart review. Results: Of the 1059 patients admitted to the ICU between 2016 and 2017, 50 patients had clinical and/or electrographic seizures (incidence of 4.7%, 95% CI: 3.4-6.0). The incidences of clinical and electrographic seizure were 4.1% and 1.1%, respectively. In a multivariable stepwise regression model, history of seizure (OR: 2.9, 95% CI: 1.1-7.8, P: .03), brain metastasis (OR: 2.5, 95% CI: 1.1-5.8, P: .03), vasopressor requirement (OR: 2.2, 95% CI: 1.0-4.9, P: .05), and age < 65 (2.4, 95% CI: 1.2-5.0, P: .02) were associated with increased risk of seizure (either clinical or electrographic). Obtaining continuous EEG instead of routine EEG increased the yield of seizure detection significantly (OR: 3.9, 95% CI: 1.3-11.1, P: .01). No chemotherapy in the past 30 days, no antibiotic use, vasopressor requirement, and having a brain tumor increased risk of electrographic seizure. Length of continuous EEG > 24 h significantly increased the chances of both clinical and electrographic seizure detection, (OR: 2.6 [95% CI: 1.2-5.7] and 15.0 [95% CI: 2.7-82.5], respectively). Conclusions: We identified known and cancer-related risk factors which can aid clinicians in diagnosing seizures in cancer ICUs. Long-term video EEG monitoring should be considered, particularly given the treatable and reversible nature of seizures.

1. Little information is available about the action of lamotrigine (LTG) on EEG paroxysmal abnormalities and background activity. On the contrary, several clinical trials have shown the therapeutic efficacy of the drug in preventing partial and generalized seizures. 2. We performed computerized EEG monitoring in 21 patients suffering from focal and generalized epilepsy before and 4 months after addition of LTG. The anticonvulsant modified the EEG ictal events by reducing their frequency and duration. A statistically significant decrease of the interictal spikes was observed. The decrease involved mainly the spreading component of the interictal events leading to a better spatial definition of the epileptic focus. 3. In the presence of LTG, generalized tonic-clonic attacks were completely controlled, whereas partial seizures were decreased. 4. The EEG background activity was not modified by the addition of the drug. 5. Our findings suggest a specific role for LTG in the generation and propagation processes of epileptiform activity without interfering with the EEG background activity.

Delay in diagnosis and subsequent treatment of nonconvulsive seizures can lead to worsened outcomes. The gold standard in detecting nonconvulsive seizures is continuous video-electroencephalography (cEEG). Compared to routine, 30-minute EEG, the use of cEEG increases the likelihood of capturing intermittent nonconvulsive seizures. Studies of critically ill patients in intensive care units demonstrate a particularly high rate of nonconvulsive seizures. Some of these studies included postneurosurgical patients, but often subanalyses of specific populations were not done. In particular, few studies have specifically evaluated postneurosurgical patients by using cEEG in the acute postoperative setting. Therefore, the incidence and predictors of acute postneurosurgical seizures are unclear. In this study, the authors focused on patients who were admitted to the neurological critical care unit following neurosurgery and who underwent cEEG monitoring within 72 hours of surgery. A total of 105 cEEG studies were performed in 102 patients. Twenty-nine patients demonstrated electrographic (subclinical) seizures, of whom 10 had clinical seizures clearly documented either before or during cEEG monitoring. Twenty-two patients had subclinical seizures only detected on cEEG, 19 of whom did not have clinical seizure activity at any point during hospitalization. Those with seizures were more likely to have had a history of epilepsy (p = 0.006). The EEG studies of patients with seizures were more likely to show lateralized periodic discharges (p = 0.012) and lateralized rhythmic delta activity (p = 0.012). The underlying neuropathological disorders most associated with seizure risk were lobar tumor on presentation (p = 0.048), subdural hematoma (SDH) requiring craniotomy for evacuation (p = 0.002), subarachnoid hemorrhage (SAH) (p = 0.026), and perioperative SAH (p = 0.019). In those undergoing craniotomy, the presence of SDH (p = 0.032), particularly if requiring evacuation (p = 0.003), increased the risk of seizures. In those without preoperative intracranial bleeding, perioperative SAH after craniotomy was associated with a higher incidence of seizures (p = 0.014). There was an additive effect on seizure incidence when perioperative SAH as well as concomitant intraparenchymal hemorrhage and/or stroke were present. The clinical examination of the patient, including the presence or absence of altered mental status and the presence or absence of repetitive movements, was not predictive of subclinical seizures. In postneurosurgical patients referred for cEEG monitoring, there is a high rate of both clinical and subclinical seizures in the early postoperative period. Seizures are particularly common in patients with SDH or lobar tumor and perioperative SAH. There was an additive effect on seizure incidence when more extensive brain injury was present. As expected, those with a history of epilepsy also demonstrated higher seizure rates. Further studies are needed to evaluate the time period of maximum seizure incidence after surgery, and the effects acute postneurosurgical seizures have on long-term outcomes.

Automatic detection of non-convulsive seizures: A reduced complexity approach

In patients with cardiac arrest who remain comatose after return of spontaneous circulation, seizures and other abnormalities on electroencephalogram (EEG) are common. Thus, guidelines recommend urgent initiation of EEG for the evaluation of seizures in this population. Point-of-care EEG systems, such as Ceribell™ Rapid Response EEG (Rapid-EEG), allow for prompt initiation of EEG monitoring, albeit through a reduced-channel montage. Rapid-EEG incorporates an automated seizure detection software (Clarity™) to measure seizure burden in real time and alert clinicians at the bedside when a high seizure burden, consistent with possible status epilepticus, is identified. External validation of Clarity is still needed. Our goal was to evaluate the real-world performance of Clarity for the detection of seizures and status epilepticus in a sample of patients with cardiac arrest. This study was a retrospective review of Rapid-EEG recordings from all the patients who were admitted to the medical intensive care unit at Kent Hospital (Warwick, RI) between 6/1/2021 and 3/18/2022 for management after cardiac arrest and who underwent Rapid-EEG monitoring as part of their routine clinical care (n = 21). Board-certified epileptologists identified events that met criteria for seizures or status epilepticus, as per the 2021 American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology, and evaluated any seizure burden detections generated by Clarity. In this study, 4 of 21 patients with cardiac arrest (19.0%) who underwent Rapid-EEG monitoring had multiple electrographic seizures, and 2 of those patients (9.5%) had electrographic status epilepticus within the first 24h of the study. None of these ictal abnormalities were detected by the Clarity seizure detection system. Clarity showed 0% seizure burden throughout the entirety of all four Rapid-EEG recordings, including the EEG pages that showed definite seizures or status epilepticus. The presence of frequent electrographic seizures and/or status epilepticus can go undetected by Clarity. Timely and careful review of all raw Rapid-EEG recordings by a qualified human EEG reader is necessary to guide clinical care, regardless of Clarity seizure burden measurements.

To assess the value and the limitations of amplitude integrated electroencephalogram (EEG) using the cerebral function monitor (CFM) in comparison with standard EEG in neonates who have hypoxic ischemic encephalopathy or were suspected of having convulsions. In 36 neonates with a gestational age > or =36 weeks, CFM and simultaneously recorded EEG traces were analyzed off-line and independently classified. CFM background activity: continuous normal voltage; continuous normal voltage, slightly discontinuous (DNV); burst-suppression (BS); continuous extremely low voltage; flat tracing. CFM epileptiform activity: suspected epileptic activity, single seizure (SS), repetitive seizures (RS), status epilepticus (SE). EEG background activity: normal, depressed, low voltage undifferentiated, excessive discontinuity, BS, no activity. Epileptiform activity: interictal unifocal, interictal multifocal, ictal unifocal, ictal multifocal, SE. A total of 33 traces were suitable for analysis. Interobserver agreement on background activity was reached in 31 cases (kappa = 0.92) for CFM and in 27 cases (kappa = 0.74) for EEG. There was full agreement on CFM ictal activity (RS, SS, or SE) and EEG ictal activity. A normal CFM (continuous normal voltage) corresponded with a normal or a depressed EEG in 90% of the cases. The positive predictive value for a severely abnormal CFM (BS, continuous extremely low voltage, flat tracing) to correspond with a severely abnormal EEG (excessive discontinuity, BS, low voltage undifferentiated, no activity) was 100% (negative predictive value, 80%; sensitivity, 76%; specificity, 100%). DNV (10) on CFM corresponded either with depressed (6) or excessive discontinuity (4) on EEG. Ictal activity on EEG corresponded with SS, RS, or SE on CFM in 8 cases (sensitivity, 80%; specificity, 100%; positive predictive value, 100%; negative predictive value, 92%). CFM is a reliable tool for monitoring both background patterns (especially normal and severely abnormal) and ictal activity. Certain focal, low amplitude, and very short periods of seizure discharges can be missed. We recommend using CFM as a monitoring device and performing intermittent standard EEG whenever there is any doubt about the classification of the CFM (ie, DNV pattern or suspected epileptiform activity).

Non-convulsive seizures (NCS) are common among critically ill children with acute encephalopathy. Continuous electroencephalogram (CEEG) monitoring is an indispensable tool to detect NCS, which is essential to guiding management and assessing prognosis. Risk factors for NCS are highest in pediatric intensive care unit (PICU) patients with altered mental status (AMS) and a recently witnessed clinical seizure, acute changes on neuroimaging, and/or interictal abnormalities on CEEG. Screening for at least 24 hours in at risk pediatric populations is ideal, but around half of NCS may be detected within the first hour. Rapid treatment of prolonged seizures or status epilepticus is critical, as higher seizure burdens have been associated with poorer outcomes in critically ill children. This review integrates current information on critically ill children with AMS and the use of CEEGs, risk factors for NCS, duration of CEEG monitoring, and how the detection of NCS impacts management and outcomes.

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.

Introduction: Continuous electroencephalography (cEEG) is essential for detecting non-convulsive seizures, status epilepticus, and tracking brain activity in critically ill patients. However, its utilization in Indian intensive care units (ICUs) varies. The purpose of this study is to evaluate current cEEG procedures, identify implementation challenges, and investigate the role of remote monitoring in improving patient care in intensive care units. Materials and Methods: In September 2024, a national survey was undertaken in Indian hospitals to assess cEEG prevalence and practices. The poll, which was circulated using Google Forms, was aimed at neurologists, neurophysiologists, intensivists, and EEG technicians. It included 20 questions about hospital characteristics, cEEG availability, use, clinical applications, and barriers. To compare public and private hospitals, data were examined using descriptive statistics and Chi-square/Fisher's exact tests. Results: There were 76 answers, with 63% of ICUs employing cEEG. Private hospitals received 50% of the responses, followed by government hospitals with 38%. Non-convulsive seizures (29%) and status epilepticus (29%) were the predominant reasons for cEEG. Barriers to adoption included a lack of skilled personnel (32%), as well as high expenses (14%). Remote EEG monitoring was employed in 37% of ICUs; however it faced problems such as unstable internet and staff opposition. Despite these challenges, 49% of respondents reported better patient outcomes using cEEG. Conclusion: Although cEEG use is increasing in major Indian institutions, problems remain. Remote monitoring could reduce obstacles and improve access, but consistent training and national guidelines are required to increase cEEG implementation in Indian ICUs.

To develop and test the effect of a nurse-led interdisciplinary intervention program for delirium on the incidence and course (severity and duration) of delirium, cognitive functioning, functional rehabilitation, mortality, and length of stay in older hip-fracture patients. Longitudinal prospective before/after design (sequential design). The emergency room and two traumatological units of an academic medical center located in an urban area in Belgium. 60 patients in an intervention cohort (81.7% females, median age = 82, interquartile range (IQR) = 13) and another 60 patients in a usual care/nonintervention cohort (80% females, median age = 80, IQR = 12). (1) Education of nursing staff, (2) systematic cognitive screening, (3) consultative services by a delirium resource nurse, a geriatric nurse specialist, or a psychogeriatrician, and (4) use of a scheduled pain protocol. All patients were monitored for signs of delirium, as measured by the Confusion Assessment Method (CAM). Severity of delirium was assessed using a variant of the CAM. Cognitive and functional status were measured by the Mini-Mental State Examination (MMSE) (including subscales of memory, linguistic ability, concentration, and psychomotor executive skills) and the Katz Index of activities of daily living (ADLs), respectively. Although there was no significant effect on the incidence of delirium (23.3% in the control vs 20.0% in the intervention cohort; P =.82), duration of delirium was shorter (P =.03) and severity of delirium was less (P =.0049) in the intervention cohort. Further, clinically higher cognitive functioning was observed for the delirious patients in the intervention cohort compared with the nonintervention cohort. Additionally, a trend toward decreased length of stay postoperatively was noted for the delirious patients in the intervention cohort. Despite these positive intervention effects, no effect on ADL rehabilitation was found. Results for risk of mortality were inconclusive. This study demonstrated the beneficial effects of an intervention program focusing on early recognition and treatment of delirium in older hip-fracture patients and confirms the reversibility of the syndrome in view of the delirium's duration and severity.

IntroductionDelirium is a common complication in the elderly after surgery and is associated with worse outcomes. Multiple risk factors are related with postoperative delirium, such as exposure to general anaesthetics,...

Nonconvulsive status epilepticus is a condition where the patient is exposed to abnormally prolonged epileptic seizures without evident physical symptoms. Since thesecontinuous seizures may cause permanent brain damage, it constitutes a medical emergency. This paper proposes a method to detect nonconvulsive seizures for a further nonconvulsive status epilepticus diagnosis. To differentiate between the normal and seizure electroencephalogram (EEG), a K-Nearest Neighbor, a Radial Basis Support Vector Machine, and a Linear Discriminant Analysis classifier are used. The classifier features are obtained from the Canonical Polyadic Decomposition (CPD) and Block Term Decomposition of the EEG data represented as third order tensor. To expand the EEG into a tensor, Wavelet or Hilbert-Huang transform are used. The algorithm is tested on a scalp EEG database of 139 seizures of different duration. The experimental results suggest that a Hilbert-Huang tensor representation and the CPD analysis provide the most suitable framework for nonconvulsive seizure detection. The Radial Basis Support Vector Machine classifier shows the best performance with sensitivity, specificity, and accuracy values over 98%. A rough comparison with other methods proposed in the literature shows the superior performance of the proposed method for nonconvulsive epileptic seizure detection.

Nonconvulsive Kainic Acid-Induced Seizures Elicit Age-Dependent Impairment of Memory for the Elevated Plus-Maze

ObjectiveIn recent years, with the development of machine learning and deep learning technologies, an increasing number of research works have begun using these technologies for automatic seizure detection in EEG signals. However, existing automatic seizure detection algorithms primarily focus on the features of individual EEG channels and pay less attention to the inter-channel relationships. This results in insufficient extraction of spatiotemporal information from multi-channel EEG data, affecting the final seizure detection performance.MethodsTherefore, this paper proposes an automatic seizure detection method based on the combination of Graph Attention Networks (GAT) and Transformer networks. Specifically, GAT is used as the front end for extracting spatial features, fully leveraging the topological structure of different EEG channels. Meanwhile, the Transformer network is used as the back end to explore temporal relationships and make final decisions based on the states before and after the current moment.ResultsExperiments were conducted on the CHB-MIT and TUH datasets with ten-fold cross-validation. The final seizure detection accuracies on the two datasets were 98.62 and 98.12%, respectively, with the model’s performance surpassing or being comparable to current state-of-the-art models.ConclusionThe proposed hybrid algorithm combines the advantages of two deep learning models, fully exploring the spatiotemporal correlations between EEG channels. Experiments on public datasets demonstrate the effectiveness of this method, significantly advancing the development of automatic seizure detection.

Epilepsy is one of the most common neurological disorders. Epileptic seizures are the manifestation of abnormal hypersynchronous discharges of cortical neurons that impair brain function. Most of the people affected can be treated successfully with drug therapy or neurosurgical procedures. But there is still a large group of epilepsy patients that continues to have frequent seizures. For these patients automated detection of epileptic seizures can be of great clinical importance. Seizure detection can influence daily care or can be used to evaluate treatment effect. Furthermore automated detection can be used to trigger an alarm system during seizures that might be harmful to the patient. This thesis focusses on accelerometry (ACM) based seizure detection. A detailed overview is provided, on the perspectives for long-term epilepsy monitoring and automated seizure detection. The value of accelerometry for seizure detection is shown by means of a clinical evaluation and the first steps are made towards automatic detection of epileptic seizures based on ACM. With accelerometers movements are recorded. A large group of epileptic seizures manifest in specific movement patterns, so called motor seizures. Chapter 2 of this thesis presents an overview of the published literature on available methods for epileptic seizure detection in a long-term monitoring context. Based on this overview recommendations are formulated that should be used in seizure detection research and development. It is shown that for seizure detection in home environments, other sensor modalities besides EEG become more important. The use of alternative sensor modalities (such as ACM) is relatively new and so is the algorithm development for seizure detection based on these measures. It was also found that for both the adaptation of existing techniques and the development of new algorithms, clinical information should be taken more into account. The value of ACM for seizure detection is shown by means of a clinical evaluation in chapter 3. Here 3-D ACM- and EEG/video-recordings of 18 patients with severe epilepsy are visually analyzed. A striking outcome presented in this chapter is the large number of visually detected seizures versus the number of seizures that was expected on forehand and the number of seizures that was observed by the nurses. These results underscore the need for an automatic seizure detection device even more, since in the current situation many seizures are missed and therefore it is possible that patients do not get the right (medical) treatment. It was also observed that 95% of the ACM-patterns during motor seizures are sequences of three elementary patterns: myoclonic, tonic and clonic patterns. These characteristic patterns are a starting point for the development of methods for automated seizure detection based on ACM. It was decided to use a modular approach for the detection methodology and develop algorithms separately for motor activity in general, myoclonic seizures and tonic seizures. Furthermore, clinical information is incorporated in the detection methodology. Therefore in this thesis features were used that are either based on the shape of the patterns of interest as described in clinical practice (chapter 4 and 7), or the features were based on a physiological model with parameters that are related to seizure duration and intensity (chapter 5 and 6). In chapter 4 an algorithm is developed to distinguish periods with and without movement from ACM-data. Hence, when there is no movement there is no motor seizure. The amount of data that needs further analysis for seizure detection is thus reduced. From 15 ACM-signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space a linear threshold function is used for classification. For training and testing the algorithm ACM data along with video data are used from nocturnal recordings in mentally retarded patients with severe epilepsy. Using this algorithm the amount of data that needs further analysis is reduced considerably. The results also indicate that the algorithm is robust for fluctuations across patients and thus there is no need for training the algorithm for each new patient. For the remaining data it needs to be established whether the detected movement is seizure related or not. To this purpose a model is developed for the accelerometer pattern measured on the arm during a myoclonic seizure (chapter 5). The model consists of a mechanical and an electrophysiological part. This model is used as a matched wavelet filter to detect myoclonic seizures. In chapter 6 the model based wavelet is compared to three other time frequency measures: the short time Fourier transform, the Wigner distribution and the continuous wavelet transform using a Daubechies wavelet. All four time-frequency methods are evaluated in a linear classification setup. Data from mentally retarded patients with severe epilepsy are used for training and evaluation. The results show that both wavelets are useful for detection of myoclonic seizures. On top of that, our model based wavelet has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiological meaningful. Besides myoclonic seizures, the model is also useful for the detection of clonic seizures; physiologically these are repetitive myoclonic seizures. Finally for the detection of tonic seizures, in chapter 7 a set of features is studied that incorporate the mean characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification in the multi-dimensional feature space. For training and testing the algorithm, again data are used from recordings in mentally retarded patients with severe epilepsy. The results show that our approach is useful for the automated detection of tonic seizures based on 3-D ACM and that it is a promising contribution in a complete multi-sensor seizure detection setup.