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Bitar R, Khan UM, Rosenthal ES. Utility and rationale for continuous EEG monitoring: a primer for the general intensivist. Crit Care 2024; 28:244. [PMID: 39014421 PMCID: PMC11251356 DOI: 10.1186/s13054-024-04986-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 06/09/2024] [Indexed: 07/18/2024] Open
Abstract
This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.
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Affiliation(s)
- Ribal Bitar
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA
| | - Usaamah M Khan
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA
| | - Eric S Rosenthal
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA.
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A Simplified Electroencephalography Montage and Interpretation for Evaluation of Comatose Patients in the ICU. Crit Care Explor 2022; 4:e0781. [DOI: 10.1097/cce.0000000000000781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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Sharma S, Nunes M, Alkhachroum A. Adult Critical Care Electroencephalography Monitoring for Seizures: A Narrative Review. Front Neurol 2022; 13:951286. [PMID: 35911927 PMCID: PMC9334872 DOI: 10.3389/fneur.2022.951286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/22/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) is an important and relatively inexpensive tool that allows intensivists to monitor cerebral activity of critically ill patients in real time. Seizure detection in patients with and without acute brain injury is the primary reason to obtain an EEG in the Intensive Care Unit (ICU). In response to the increased demand of EEG, advances in quantitative EEG (qEEG) created an approach to review large amounts of data instantly. Finally, rapid response EEG is now available to reduce the time to detect electrographic seizures in limited-resource settings. This review article provides a concise overview of the technical aspects of EEG monitoring for seizures, clinical indications for EEG, the various available modalities of EEG, common and challenging EEG patterns, and barriers to EEG monitoring in the ICU.
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Affiliation(s)
- Sonali Sharma
- Department of Neurology, University of Miami, Miami, FL, United States
- Department of Neurology, Jackson Memorial Hospital, Miami, FL, United States
| | - Michelle Nunes
- Department of Neurology, University of Miami, Miami, FL, United States
- Department of Neurology, Jackson Memorial Hospital, Miami, FL, United States
| | - Ayham Alkhachroum
- Department of Neurology, University of Miami, Miami, FL, United States
- Department of Neurology, Jackson Memorial Hospital, Miami, FL, United States
- *Correspondence: Ayham Alkhachroum
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McCredie VA. Sonification of Seizures: Music to Our Ears. Crit Care Med 2021; 48:1383-1385. [PMID: 32826490 DOI: 10.1097/ccm.0000000000004483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Victoria A McCredie
- Interdepartmental Division of Critical Care Medicine, University of Toronto; Department of Critical Care Medicine Toronto Western Hospital University Health Network; and Krembil Research Institute, Toronto Western Hospital, Toronto, ON, Canada
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Taran S, Ahmed W, Bui E, Prisco L, Hahn CD, McCredie VA. Educational initiatives and implementation of electroencephalography into the acute care environment: a protocol of a systematic review. Syst Rev 2020; 9:175. [PMID: 32778151 PMCID: PMC7418425 DOI: 10.1186/s13643-020-01439-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 07/29/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Use of electroencephalography (EEG) is currently recommended by the American Clinical Neurophysiology Society for a wide range of indications, including diagnosis of nonconvulsive status epilepticus and evaluation of unexplained disorders of consciousness. Data interpretation usually occurs by expert personnel (e.g., epileptologists, neurophysiologists), with information relayed to the primary care team. However, data cannot always be read in time-sensitive fashion, leading to potential delays in EEG interpretation and patient management. Multiple training programs have recently been described to enable non-experts to rapidly interpret EEG at the bedside. A comprehensive review of these training programs, including the tools used, outcomes obtained, and potential pitfalls, is currently lacking. Therefore, the optimum training program and implementation strategy remain unknown. METHODS We will conduct a systematic review of descriptive studies, case series, cohort studies, and randomized controlled trials assessing training programs for EEG interpretation by non-experts. Our primary objective is to comprehensively review educational programs in this domain and report their structure, patterns of implementation, limitations, and trainee feedback. Our secondary objective will be to compare the performance of non-experts for EEG interpretation with a gold standard (e.g., interpretation by a certified electroencephalographers). Studies will be limited to those performed in acute care settings in both adult and pediatric populations (intensive care unit, emergency department, or post-anesthesia care units). Comprehensive search strategies will be developed for MEDLINE, EMBASE, WoS, CINAHL, and CENTRAL to identify studies for review. The gray literature will be scanned for further eligible studies. Two reviewers will independently screen the search results to identify studies for inclusion. A standardized data extraction form will be used to collect important data from each study. If possible, we will attempt to meta-analyze the quantitative data. If heterogeneity between studies is too high, we will present meaningful quantitative comparisons of secondary outcomes as per the synthesis without meta-analysis (SWiM) reporting guidelines. DISCUSSION We will aim to summarize the current literature in this domain to understand the structure, patterns, and pitfalls of EEG training programs for non-experts. This review is undertaken with a view to inform future education designs, potentially enabling rapid detection of EEG abnormalities, and timely intervention by the treating physician. PROSPERO REGISTRATION Submitted and undergoing review. Registration ID: CRD42020171208 .
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Affiliation(s)
- Shaurya Taran
- Interdepartmental Division of Critical Care, Li Ka Shing Knowledge Institute, 204 Victoria Street, 4th Floor, Room 411, Toronto, Ontario, M5B 1T8, Canada.
| | - Wael Ahmed
- Department of Critical Care Medicine, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Esther Bui
- Division of Neurology, University Health Network, Toronto, Ontario, Canada
| | - Lara Prisco
- Neurosciences Intensive Care Unit, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children, and Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Victoria A McCredie
- Interdepartmental Division of Critical Care, Li Ka Shing Knowledge Institute, 204 Victoria Street, 4th Floor, Room 411, Toronto, Ontario, M5B 1T8, Canada.,Department of Critical Care Medicine, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada.,Division of Critical Care Medicine, Department of Medicine, University Health Network, Toronto, Ontario, Canada.,Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
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Ghossein J, Alnaji F, Webster RJ, Bulusu S, Pohl D. Continuous EEG in a Pediatric Intensive Care Unit: Adherence to Monitoring Criteria and Barriers to Adequate Implementation. Neurocrit Care 2020; 34:519-528. [PMID: 32696100 DOI: 10.1007/s12028-020-01053-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 07/09/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Subclinical seizures are common in critically ill children and are best detected by continuous EEG (cEEG) monitoring. Timely detection of seizures requires pediatric intensive care unit (PICU) physicians to identify patients at risk of seizures and request cEEG monitoring. A recent consensus statement from the American Clinical Neurophysiology Society (ACNS) outlines the indications for cEEG monitoring in critically ill patients. However, adherence to these cEEG monitoring criteria among PICU physicians is unknown. Our project had two goals: 1. To assess adherence to cEEG monitoring indications and barriers toward their implementation; 2. To improve compliance with the ACNS cEEG monitoring criteria in our PICU. METHODS This is a single-institution study. A total of 234 PICU admissions (183 unique patients) were studied. A 6-month retrospective chart review identified PICU patients meeting ACNS criteria for cEEG monitoring, and patients for whom monitoring was requested. This was followed by an 8-week quality improvement project. During this mentorship period, a didactic 15-min lecture and summary handouts regarding the ACNS indications for cEEG monitoring were provided to all PICU physicians. Requests for cEEG monitoring during the mentorship period were compared to baseline adherence to cEEG monitoring recommendations, and barriers toward timely cEEG monitoring were assessed. RESULTS Nearly every fifth PICU patient met cEEG monitoring indications, and prevalences of patients meeting those indications were similar in the retrospective and the prospective mentorship period (18% vs. 19%). Almost all patients (98%) requiring cEEG as per ACNS criteria met the indication for monitoring already at the time of their PICU admission. During the retrospective period, 23% of patients meeting ACNS criteria had a request for cEEG monitoring, which increased to 83% during the mentorship period. The median delay to cEEG initiation was 16.7 h during the mentorship period, largely due to limited hours of EEG technician availability. Electrographic seizures were identified in 36% of patients monitored, all within the first 120 min of cEEG recording. The majority (79%) of cEEGs informed clinical management. CONCLUSIONS A brief teaching intervention supplemented by pictographic handouts significantly increased adherence to cEEG monitoring recommendations, and cEEGs guided clinical management. However, there were long delays to cEEG initiation. In order to promptly recognize subclinical seizures in critically ill children, we strongly advocate for a routine screening for cEEG monitoring indications as part of the PICU admission process, and a care model allowing for cEEG initiation around-the-clock.
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Affiliation(s)
- Jamie Ghossein
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Fuad Alnaji
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.,Children's Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada.,CHEO Research Institute, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada
| | - Richard J Webster
- CHEO Research Institute, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada
| | - Srinivas Bulusu
- Children's Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada
| | - Daniela Pohl
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada. .,Children's Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada. .,CHEO Research Institute, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada.
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Egawa S, Hifumi T, Nakamoto H, Kuroda Y, Kubota Y. Diagnostic Reliability of Headset-Type Continuous Video EEG Monitoring for Detection of ICU Patterns and NCSE in Patients with Altered Mental Status with Unknown Etiology. Neurocrit Care 2020; 32:217-225. [PMID: 31617115 DOI: 10.1007/s12028-019-00863-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND/OBJECTIVE Simplified continuous electroencephalogram (cEEG) monitoring has shown improvement in detecting seizures; however, it is insufficient in detecting abnormal EEG patterns, such as periodic discharges (PDs), rhythmic delta activity (RDA), spikes and waves (SW), and continuous slow wave (CS), as well as nonconvulsive status epilepticus (NCSE). Headset-type continuous video EEG monitoring (HS-cv EEG monitoring; AE-120A EEG Headset™, Nihon Kohden, Tokyo, Japan) is a recently developed easy-to-use technology with eight channels. However, its ability to detect abnormal EEG patterns with raw EEG data has not been comprehensively evaluated. We aimed to examine the diagnostic accuracy of HS-cv EEG monitoring in detecting abnormal EEG patterns and NCSE in patients with altered mental status (AMS) with unknown etiology. We also evaluated the time required to initiate HS-cv EEG monitoring in these patients. METHODS We prospectively observed and retrospectively examined patients who were admitted with AMS between January and December 2017 at the neurointensive care unit at Asakadai Central General Hospital, Saitama, Japan. We excluded patients whose data were missing for various reasons, such as difficulties in recording, and those whose consciousness had recovered between HS-cv EEG and conventional cEEG (C-cEEG) monitoring. For the included patients, we performed HS-cv EEG monitoring followed by C-cEEG monitoring. Definitive diagnosis was confirmed by C-cEEG monitoring with the international 10-20 system. As the primary outcome, we verified the sensitivity and specificity of HS-cv EEG monitoring in detecting abnormal EEG patterns including PDs, RDA, SW, and CS, in detecting the presence of PDs, and in detecting NCSE. As the secondary outcome, we calculated the time to initiate HS-cv EEG monitoring after making the decision. RESULTS Fifty patients (76.9%) were included in the final analyses. The median age was 72 years, and 66% of the patients were male. The sensitivity and specificity of HS-cv EEG monitoring for detecting abnormal EEG patterns were 0.974 (0.865-0.999) and 0.909 (0.587-0.998), respectively, and for detecting PDs were 0.824 (0.566-0.926) and 0.970 (0.842-0.999), respectively. We diagnosed 13 (26%) patients with NCSE using HS-cv EEG monitoring and could detect NCSE with a sensitivity and specificity of 0.706 (0.440-0.897) and 0.970 (0.842-0.999), respectively. The median time needed to initiate HS-cv EEG was 57 min (5-142). CONCLUSIONS HS-cv EEG monitoring is highly reliable in detecting abnormal EEG patterns, with moderate reliability for PDs and NCSE, and rapidly initiates cEEG monitoring in patients with AMS with unknown etiology.
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Affiliation(s)
- Satoshi Egawa
- Neurointensive Care Unit, Department of Neurosurgery, and Stroke and Epilepsy Center, TMG Asaka Medical Center, Saitama, Japan
| | - Toru Hifumi
- Department of Emergency and Critical Care Medicine, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan.
| | - Hidetoshi Nakamoto
- Department of Neurosurgery, Saiseikai Kurihashi Hospital, Saitama, Japan
| | - Yasuhiro Kuroda
- Emergency Medical Center, Kagawa University Hospital, Kagawa, Japan
| | - Yuichi Kubota
- Department of Neurosurgery, and Stroke and Epilepsy Center, TMG Asaka Medical Center, Saitama, Japan
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Dehzangi O, Farooq M. Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification. BIOMED RESEARCH INTERNATIONAL 2018; 2018:9796238. [PMID: 29662908 PMCID: PMC5832111 DOI: 10.1155/2018/9796238] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 12/01/2017] [Accepted: 12/06/2017] [Indexed: 11/17/2022]
Abstract
A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means. Patients rated most communication sessions as difficult and unsuccessful. This, in turn, can cause distress, unrecognized pain, anxiety, and fear. As such, we designed a portable BCI system for ICU communications (BCI4ICU) optimized to operate effectively in an ICU environment. The system utilizes a wearable EEG cap coupled with an Android app designed on a mobile device that serves as visual stimuli and data processing module. Furthermore, to overcome the challenges that BCI systems face today in real-world scenarios, we propose a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm. First, we incorporate subject-specific information in the training phase of the SSVEP identification model using GMM-based training and adaptation. We evaluate subject-specific models against other subjects. Subsequently, from the GMM discriminative scores, we generate the transformed vectors, which are passed to our predictive model. Finally, the adapted mixture mean scores of the subject-specific GMMs are utilized to generate the high-dimensional supervectors. Our experimental results demonstrate that the proposed system achieved 98.7% average identification accuracy, which is promising in order to provide effective and consistent communication for patients in the intensive care.
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Affiliation(s)
- Omid Dehzangi
- Computer and Information Science Department, University of Michigan-Dearborn, 4901 Evergreen Rd., CIS 112, Dearborn, MI, USA
| | - Muhamed Farooq
- Computer and Information Science Department, University of Michigan-Dearborn, 4901 Evergreen Rd., CIS 112, Dearborn, MI, USA
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