The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Lawrence J Hirsch - One of the best experts on this subject based on the ideXlab platform.
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time dependent risk of seizures in critically ill patients on continuous electroencephalogram
Annals of Neurology, 2017Co-Authors: Aaron F Struck, Brandon M Westover, Gamaleldin Osman, Nishi Rampal, Siddhartha Biswal, Benjamin Legros, Lawrence J Hirsch, Nicolas GaspardAbstract:Objective Find the optimal continuous electroencephalographic (CEeg) monitoring duration for seizure detection in critically ill patients. Methods We analyzed prospective data from 665 consecutive CEegs, including clinical factors and time-to-event emergence of electroencephalographic (Eeg) findings over 72 hours. Clinical factors were selected using logistic regression. Eeg risk factors were selected a priori. Clinical factors were used for baseline (pre-Eeg) risk. Eeg findings were used for the creation of a multistate survival model with 3 states (entry, Eeg risk, and seizure). Eeg risk state is defined by emergence of epileptiform patterns. Results The clinical variables of greatest predictive value were coma (31% had seizures; odds ratio [OR] = 1.8, p < 0.01) and history of seizures, either remotely or related to acute illness (34% had seizures; OR = 3.0, p < 0.001). If there were no epileptiform findings on Eeg, the risk of seizures within 72 hours was between 9% (no clinical risk factors) and 36% (coma and history of seizures). If epileptiform findings developed, the seizure incidence was between 18% (no clinical risk factors) and 64% (coma and history of seizures). In the absence of epileptiform Eeg abnormalities, the duration of monitoring needed for seizure risk of <5% was between 0.4 hours (for patients who are not comatose and had no prior seizure) and 16.4 hours (comatose and prior seizure). Interpretation The initial risk of seizures on CEeg is dependent on history of prior seizures and presence of coma. The risk of developing seizures on CEeg decays to <5% by 24 hours if no epileptiform Eeg abnormalities emerge, independent of initial clinical risk factors. Ann Neurol 2017;82:177–185.
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continuous electroencephalogram monitoring in the intensive care unit
Anesthesia & Analgesia, 2009Co-Authors: Daniel Friedman, Jan Claassen, Lawrence J HirschAbstract:Because of recent technical advances, it is now possible to record and monitor the continuous digital electroencephalogram (Eeg) of many critically ill patients simultaneously. Continuous Eeg monitoring (cEeg) provides dynamic information about brain function that permits early detection of changes in neurologic status, which is especially useful when the clinical examination is limited. Nonconvulsive seizures are common in comatose critically ill patients and can have multiple negative effects on the injured brain. The majority of seizures in these patients cannot be detected without cEeg. cEeg monitoring is most commonly used to detect and guide treatment of nonconvulsive seizures, including after convulsive status epilepticus. In addition, cEeg is used to guide management of pharmacological coma for treatment of increased intracranial pressure. An emerging application for cEeg is to detect new or worsening brain ischemia in patients at high risk, especially those with subarachnoid hemorrhage. Improving quantitative Eeg software is helping to make it feasible for cEeg (using full scalp coverage) to provide continuous information about changes in brain function in real time at the bedside and to alert clinicians to any acute brain event, including seizures, ischemia, increasing intracranial pressure, hemorrhage, and even systemic abnormalities affecting the brain, such as hypoxia, hypotension, acidosis, and others. Monitoring using only a few electrodes or using full scalp coverage, but without expert review of the raw Eeg, must be done with extreme caution as false positives and false negatives are common. Intracranial Eeg recording is being performed in a few centers to better detect seizures, ischemia, and peri-injury depolarizations, all of which may contribute to secondary injury. When cEeg is combined with individualized, physiologically driven decision making via multimodality brain monitoring, intensivists can identify when the brain is at risk for injury or when neuronal injury is already occurring and intervene before there is permanent damage. The exact role and cost-effectiveness of cEeg at the current time remains unclear, but we believe it has significant potential to improve neurologic outcomes in a variety of settings.
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detection of electrographic seizures with continuous Eeg monitoring in critically ill patients
Neurology, 2004Co-Authors: Jan Claassen, Stephan A Mayer, Robert G Kowalski, Ronald G Emerson, Lawrence J HirschAbstract:Objective: To identify patients most likely to have seizures documented on continuous Eeg (cEeg) monitoring and patients who require more prolonged cEeg to record the first seizure. Methods: Five hundred seventy consecutive patients who underwent cEeg monitoring over a 6.5-year period were reviewed for the detection of subclinical seizures or evaluation of unexplained decrease in level of consciousness. Baseline demographic, clinical, and Eeg findings were recorded and a multivariate logistic regression analysis performed to identify factors associated with 1) any Eeg seizure activity and 2) first seizure detected after >24 hours of monitoring. Results: Seizures were detected in 19% (n = 110) of patients who underwent cEeg monitoring; the seizures were exclusively nonconvulsive in 92% (n = 101) of these patients. Among patients with seizures, 89% (n = 98) were in intensive care units at the time of monitoring. Electrographic seizures were associated with coma (odds ratio [OR] 7.7, 95% CI 4.2 to 14.2), age 24 hours of monitoring (20% vs 5% of noncomatose patients; OR 4.5, p = 0.018). Conclusions: CEeg monitoring detected seizure activity in 19% of patients, and the seizures were almost always nonconvulsive. Coma, age 24 hours of monitoring to detect the first electrographic seizure.
Jan G Jakobsson - One of the best experts on this subject based on the ideXlab platform.
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use of conventional ecg electrodes for depth of anaesthesia monitoring using the cerebral state index a clinical study in day surgery
BJA: British Journal of Anaesthesia, 2007Co-Authors: R E Anderson, Ulrik Sartipy, Jan G JakobssonAbstract:Background The cost–benefit relationship for depth of anaesthesia monitors is complicated by the high cost of specially designed Eeg electrodes. The cerebral state index (CSI) monitor will accept regular ECG electrodes with snap connectors. The purpose of this study was to determine if generic ECG electrodes could replace the more expensive proprietary Eeg electrodes for the CSI monitor. Methods Two identical cerebral state monitors were used simultaneously during sevoflurane anaesthesia for knee arthroscopy in 14 ASA I–II patients. One monitor used proprietary (Danmeter) Eeg electrodes and the other used ECG electrodes (3M™ Red Dot™ Diagnostic ECG Electrodes). Paired CSI values were recorded every other minute. Anaesthetic depth was titrated clinically. Sedation depth was scored according to the Observer's Assessment of Alertness/Sedation (OAAS) scale. Results The agreement between the two measures was found to be high, mean difference − 0.23, and the overall repeatability mean bias was 6.6 and 153/163 pairs (94%) were located within the 95% limits of agreement. No major difference was noted in impedance, noise, or artifacts. A large overlap in CSI was noted for each level of the OAAS scale; patients with CSI values as low as 40–50 responded whereas patients not responding to surgical stimulation had CSI values as high as 75. The direct cost of disposables decreased from 4€ to 0.50€ per patient by using ordinary ECG electrodes. Conclusions Switching from proprietary Eeg electrodes to ordinary generic ECG electrodes maintains the same accuracy at about a 10th of the cost when measuring CSI during day surgery with sevoflurane anaesthesia.
Brandon M Westover - One of the best experts on this subject based on the ideXlab platform.
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rapid annotation of seizures and interictal ictal injury continuum Eeg patterns
Journal of Neuroscience Methods, 2021Co-Authors: Jin Jing, Mohammad Tabaeizadeh, Emile Dangremont, Senan Ebrahim, Aline Herlopian, Justin Dauwels, Brandon M WestoverAbstract:Abstract Background Manual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous Eeg (cEeg) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEeg. New method We learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEeg recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. Eeg experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters. Results Mean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ± 4.44 min to label the 30.19 ± 3.84 h of cEeg data, more than 45 times faster than unaided manual review, demonstrating efficiency. Comparison with existing methods Previous studies of Eeg data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. Conclusions Our results suggest that long Eeg recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.
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time dependent risk of seizures in critically ill patients on continuous electroencephalogram
Annals of Neurology, 2017Co-Authors: Aaron F Struck, Brandon M Westover, Gamaleldin Osman, Nishi Rampal, Siddhartha Biswal, Benjamin Legros, Lawrence J Hirsch, Nicolas GaspardAbstract:Objective Find the optimal continuous electroencephalographic (CEeg) monitoring duration for seizure detection in critically ill patients. Methods We analyzed prospective data from 665 consecutive CEegs, including clinical factors and time-to-event emergence of electroencephalographic (Eeg) findings over 72 hours. Clinical factors were selected using logistic regression. Eeg risk factors were selected a priori. Clinical factors were used for baseline (pre-Eeg) risk. Eeg findings were used for the creation of a multistate survival model with 3 states (entry, Eeg risk, and seizure). Eeg risk state is defined by emergence of epileptiform patterns. Results The clinical variables of greatest predictive value were coma (31% had seizures; odds ratio [OR] = 1.8, p < 0.01) and history of seizures, either remotely or related to acute illness (34% had seizures; OR = 3.0, p < 0.001). If there were no epileptiform findings on Eeg, the risk of seizures within 72 hours was between 9% (no clinical risk factors) and 36% (coma and history of seizures). If epileptiform findings developed, the seizure incidence was between 18% (no clinical risk factors) and 64% (coma and history of seizures). In the absence of epileptiform Eeg abnormalities, the duration of monitoring needed for seizure risk of <5% was between 0.4 hours (for patients who are not comatose and had no prior seizure) and 16.4 hours (comatose and prior seizure). Interpretation The initial risk of seizures on CEeg is dependent on history of prior seizures and presence of coma. The risk of developing seizures on CEeg decays to <5% by 24 hours if no epileptiform Eeg abnormalities emerge, independent of initial clinical risk factors. Ann Neurol 2017;82:177–185.
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accuracy of limited montage electroencephalography in monitoring postanoxic comatose patients
Clinical Eeg and Neuroscience, 2017Co-Authors: Sandipan Pati, Lauren M Mcclain, Lidia M V R Moura, Yuan Fan, Brandon M WestoverAbstract:Background. Continuous Eeg (cEeg) monitoring may help to identify the small percentage of adults with hypoxic-ischemic encephalopathy (HIE) who will regain consciousness if allowed sufficient time. However, the limited yield in this population has led some to question the cost-effectiveness cEeg monitoring in this population. We hypothesized that limited-montage cEeg could provide essentially the same neurophysiologic information at lower cost. In this proof of concept study, we aim to demonstrate the potentials of limited channel Eeg in prognostication in postanoxic patients. Methods. We retrospectively reviewed cEeg data from cases monitored at our institution with conventional 21-channel Eeg over a 6-month period. Twenty-eight cases were identified in which patients with HIE underwent cEeg for at least 24 hours. Gold-standard findings were determined by conventional visual analysis of the full cEeg, and 2 independent electroencephalographers scored the same data using only limited-montage (4-channel) v...
Nitish V Thakor - One of the best experts on this subject based on the ideXlab platform.
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quantitative assessment of the training improvement in a motor cognitive task by using Eeg ecg and eog signals
Brain Topography, 2016Co-Authors: Gianluca Borghini, Pietro Arico, Ilenia Graziani, S Salinari, Yu Sun, Fumihiko Taya, Anastatios Bezerianos, Nitish V Thakor, Fabio BabiloniAbstract:Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator’s skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throughout the training period by analyzing neuro-physiological signals, such as the electroencephalogram, the electrocardiogram and the electrooculogram. In fact, it is well known that such signals correlate with a variety of cognitive processes, e.g. attention, information processing, and working memory. A group of 10 subjects have been asked to train daily with the NASA multi-attribute-task-battery. During such training period the neuro-physiological, behavioral and subjective data have been collected. In particular, the neuro-physiological signals have been recorded on the first (T1), on the third (T3) and on the last training day (T5), while the behavioral and subjective data have been collected every day. Finally, all these data have been compared for a complete overview of the learning process and its relations with the neuro-physiological parameters. It has been shown how the integration of brain activity, in the theta and alpha frequency bands, with the autonomic parameters of heart rate and eyeblink rate could be used as metric for the evaluation of the learning progress, as well as the final training level reached by the subjects, in terms of request of cognitive resources.
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describing the nonstationarity level of neurological signals based on quantifications of time frequency representation
IEEE Transactions on Biomedical Engineering, 2007Co-Authors: Shanbao Tong, Yisheng Zhu, Nitish V ThakorAbstract:Most neurological signals including electroencephalogram (Eeg), evoked potential (EP) and local field potential (LFP) have been known to be time varying and nonstationary, especially in some pathological conditions. Currently, the most widely used quantitative tool for such nonstationary signals is time-frequency representation (TFR) which demonstrates the temporal evolution of different frequency components. However, TFR does not directly provide a quantitative measure of nonstationarity level, e.g., how far the process deviates from stationarity. In this study, we introduced three different quantifications of TFR (qTFR) to characterize the nonstationarity level of the involving signals: 1) degree of stationarity (DS); 2) Shannon entropy (SE) of the marginal spectrum; and 3) Kullback-Leibler distance (KLD) between a TFR and a uniform distribution. These descriptors provide quantitative analysis of stationarity of a signal such that the stationarity of different signals could be compared. In this study, we obtained the TFRs of the Eeg signals before and after the hypoxic-ischemic (HI) brain injury and examined the stationarity of the Eeg. DS, SE, and KLD can indicate the nonstationarity change of Eeg at each frequency following the HI injury, especially in the upperdelta-and lower thetas-band (e.g., [2 Hz, 8 Hzi) as well as in the beta2 band (e.g., [22 Hz-26 Hzi). Moreover, it is shown that the stationarity of the Eeg changes differently in different frequencies following the HI injury.
Jan Claassen - One of the best experts on this subject based on the ideXlab platform.
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continuous electroencephalogram monitoring in the intensive care unit
Anesthesia & Analgesia, 2009Co-Authors: Daniel Friedman, Jan Claassen, Lawrence J HirschAbstract:Because of recent technical advances, it is now possible to record and monitor the continuous digital electroencephalogram (Eeg) of many critically ill patients simultaneously. Continuous Eeg monitoring (cEeg) provides dynamic information about brain function that permits early detection of changes in neurologic status, which is especially useful when the clinical examination is limited. Nonconvulsive seizures are common in comatose critically ill patients and can have multiple negative effects on the injured brain. The majority of seizures in these patients cannot be detected without cEeg. cEeg monitoring is most commonly used to detect and guide treatment of nonconvulsive seizures, including after convulsive status epilepticus. In addition, cEeg is used to guide management of pharmacological coma for treatment of increased intracranial pressure. An emerging application for cEeg is to detect new or worsening brain ischemia in patients at high risk, especially those with subarachnoid hemorrhage. Improving quantitative Eeg software is helping to make it feasible for cEeg (using full scalp coverage) to provide continuous information about changes in brain function in real time at the bedside and to alert clinicians to any acute brain event, including seizures, ischemia, increasing intracranial pressure, hemorrhage, and even systemic abnormalities affecting the brain, such as hypoxia, hypotension, acidosis, and others. Monitoring using only a few electrodes or using full scalp coverage, but without expert review of the raw Eeg, must be done with extreme caution as false positives and false negatives are common. Intracranial Eeg recording is being performed in a few centers to better detect seizures, ischemia, and peri-injury depolarizations, all of which may contribute to secondary injury. When cEeg is combined with individualized, physiologically driven decision making via multimodality brain monitoring, intensivists can identify when the brain is at risk for injury or when neuronal injury is already occurring and intervene before there is permanent damage. The exact role and cost-effectiveness of cEeg at the current time remains unclear, but we believe it has significant potential to improve neurologic outcomes in a variety of settings.
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detection of electrographic seizures with continuous Eeg monitoring in critically ill patients
Neurology, 2004Co-Authors: Jan Claassen, Stephan A Mayer, Robert G Kowalski, Ronald G Emerson, Lawrence J HirschAbstract:Objective: To identify patients most likely to have seizures documented on continuous Eeg (cEeg) monitoring and patients who require more prolonged cEeg to record the first seizure. Methods: Five hundred seventy consecutive patients who underwent cEeg monitoring over a 6.5-year period were reviewed for the detection of subclinical seizures or evaluation of unexplained decrease in level of consciousness. Baseline demographic, clinical, and Eeg findings were recorded and a multivariate logistic regression analysis performed to identify factors associated with 1) any Eeg seizure activity and 2) first seizure detected after >24 hours of monitoring. Results: Seizures were detected in 19% (n = 110) of patients who underwent cEeg monitoring; the seizures were exclusively nonconvulsive in 92% (n = 101) of these patients. Among patients with seizures, 89% (n = 98) were in intensive care units at the time of monitoring. Electrographic seizures were associated with coma (odds ratio [OR] 7.7, 95% CI 4.2 to 14.2), age 24 hours of monitoring (20% vs 5% of noncomatose patients; OR 4.5, p = 0.018). Conclusions: CEeg monitoring detected seizure activity in 19% of patients, and the seizures were almost always nonconvulsive. Coma, age 24 hours of monitoring to detect the first electrographic seizure.