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Chung YG, Cho A, Kim H, Kim KJ. Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset. Front Neurol 2024; 15:1389731. [PMID: 38836000 PMCID: PMC11148866 DOI: 10.3389/fneur.2024.1389731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024] Open
Abstract
Introduction Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients. Methods We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation. Results Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones. Discussion We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.
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Affiliation(s)
- Yoon Gi Chung
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Anna Cho
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Hunmin Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki Joong Kim
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
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Yu Z, Guo S. A low-cost, wireless, 4-channel EEG measurement system used in virtual reality environments. HARDWAREX 2024; 17:e00507. [PMID: 38327677 PMCID: PMC10847955 DOI: 10.1016/j.ohx.2024.e00507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/20/2023] [Accepted: 01/19/2024] [Indexed: 02/09/2024]
Abstract
The combination of Virtual Reality (VR) technology and Electroencephalography (EEG) measurements has shown tremendous potential in the fields of psychology and neuroscience research. However, the majority of EEG measurement devices currently available are expensive, bulky, uncomfortable to wear, and difficult to integrate with VR headsets. These limitations have hindered the development of related research fields. This study describes a low-cost (60.07 USD), small-sized, wireless, high-precision, low-power consumption 4-channel EEG measurement system (NeuroVista) for frontal area EEG measurements, which can be used with a VR headset, enabling EEG measurements in VR environments. The system has an input-referred noise of less than 0.9480 μ V r m s , a common mode rejection ratio of over 96 dB, a measurement resolution of less than 0.1 μ V , a bandwidth of 0.5 ∼ 45 Hz, and works at a sampling rate of 250 Hz. It also supports metal dry electrodes and includes a built-in analog bandpass filter, right-leg drive circuit, and built-in digital lowpass filter and notch filter, which can reduce noise during measurement. Researchers can reconstruct the electrode system to measure regions of interest according to their needs.
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Affiliation(s)
- Zhiyuan Yu
- Department of Biomedical Engineering, School of Materials, South China University of Technology, Guangdong Province, China
| | - Shengwen Guo
- Department of Intelligent Science and Engineering, School of Automation, South China University of Technology, Guangdong Province, China
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3
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González Otárula KA, Schuele S. Ambulatory EEG-video. Epilepsy Behav 2024; 151:109615. [PMID: 38176091 DOI: 10.1016/j.yebeh.2023.109615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/24/2023] [Accepted: 12/27/2023] [Indexed: 01/06/2024]
Abstract
Hospital based EEG recordings have been the norm to assist in the diagnosis and management of patients with unclassified events and known drug resistant epilepsy. Ambulatory EEG (AEEG) is a tool that comes to serve the needs for a portable testing that can be done at home, often with higher accessibility compared to an epilepsy monitoring unit and with lower cost. The current technology provides good quality EEG tracing and can be done with video when needed. In this review we discuss how AEEG should be performed and the preferred indications in which this test may be of utmost help. The advent of ultra-long ambulatory recording may be the future for selected patients as this technology evolves.
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Affiliation(s)
| | - Stephan Schuele
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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4
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Ulate-Campos A, Loddenkemper T. Review on the current long-term, limited lead electroencephalograms. Epilepsy Behav 2024; 150:109557. [PMID: 38070411 DOI: 10.1016/j.yebeh.2023.109557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/14/2023] [Accepted: 11/17/2023] [Indexed: 01/14/2024]
Abstract
In the last century, 10-20 lead EEG recordings became the gold standard of surface EEG recordings, and the 10-20 system provided comparability between international studies. With the emergence of advanced EEG sensors, that may be able to record and process signals in much more compact units, this additional sensor technology now opens up opportunities to revisit current ambulatory EEG recording practices and specific patient populations, and even electrodes that are embedded into the head surface. Here, we aim to provide an overview of current limited sensor long-term EEG systems. We performed a literature review using Pubmed as a database and included the relevant articles. The review identified several systems for recording long-term ambulatory EEGs. In general, EEGs recorded with these modalities can be acquired in ambulatory and home settings, achieve good sensitivity with low false detection rates, are used for automatic seizure detection as well as seizure forecasting, and are well tolerated by patients, but each of them has advantages and disadvantages. Subcutaneous, subgaleal, and subscalp electrodes are minimally invasive and provide stable signals that can record ultra--long-term EEG and are in general less noisy than scalp EEG, but they have limited spatial coverage and require anesthesia, a surgical procedure and a trained surgeon to be placed. Behind and in the ear electrodes are discrete, unobtrusive with a good sensitivity mainly for temporal seizures but might miss extratemporal seizures, recordings could be obscured by muscle artifacts and bilateral ictal patterns might be difficult to register. Finally, recording systems using electrodes in a headband can be easily and quickly placed by the patient or caregiver, but have less spatial coverage and are more prone to movement because electrodes are not attached. Overall, limited EEG recording systems offer a promising opportunity to potentially record targeted EEG with focused indications for prolonged periods, but further validation work is needed.
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Hartmann M, Koren J, Baumgartner C, Duun-Henriksen J, Gritsch G, Kluge T, Perko H, Fürbass F. Seizure detection with deep neural networks for review of two-channel electroencephalogram. Epilepsia 2023; 64 Suppl 4:S34-S39. [PMID: 35416283 DOI: 10.1111/epi.17259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 11/27/2022]
Abstract
Ultra-long-term electroencephalographic (EEG) registration using minimally invasive low-channel devices is an emerging technology to assess sporadic seizure events. Highly sensitive automatic seizure detection algorithms are needed for semiautomatic evaluation of these prolonged recordings. We describe the design and validation of a deep neural network for two-channel seizure detection. The model is trained using EEG recordings from 590 patients in a publicly available seizure database. These recordings are based on the full 10-20 electrode system and include seizure annotations created by reviews of the full set of EEG channels. Validation was performed using 48 scalp EEG recordings from an independent epilepsy center and consensus seizure annotations from three neurologists. For each patient, a three-electrode subgroup (two channels with a common reference) of the full montage was selected for validation of the two-channel model. Mean sensitivity across patients of 88.8% and false positive rate across patients of 12.9/day were achieved. The proposed training approach is of great practical relevance, because true recordings from low-channel devices are currently available only in small numbers, and the generation of gold standard seizure annotations in two EEG channels is often difficult. The study demonstrates that automatic seizure detection based on two-channel EEG data is feasible and review of ultra-long-term recordings can be made efficient and effective.
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Affiliation(s)
- Manfred Hartmann
- Center for Health and Bioresources, Austrian Institute of Technology, Vienna, Austria
| | - Johannes Koren
- Department of Neurology, Clinic Hietzing, Vienna, Austria
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
| | - Christoph Baumgartner
- Department of Neurology, Clinic Hietzing, Vienna, Austria
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
- Medical Faculty, Sigmund Freud University, Vienna, Austria
| | | | - Gerhard Gritsch
- Center for Health and Bioresources, Austrian Institute of Technology, Vienna, Austria
| | - Tilmann Kluge
- Center for Health and Bioresources, Austrian Institute of Technology, Vienna, Austria
| | - Hannes Perko
- Center for Health and Bioresources, Austrian Institute of Technology, Vienna, Austria
| | - Franz Fürbass
- Center for Health and Bioresources, Austrian Institute of Technology, Vienna, Austria
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Knight A, Gschwind T, Galer P, Worrell GA, Litt B, Soltesz I, Beniczky S. Artificial intelligence in epilepsy phenotyping. Epilepsia 2023:10.1111/epi.17833. [PMID: 37983589 PMCID: PMC11102939 DOI: 10.1111/epi.17833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 11/17/2023] [Indexed: 11/22/2023]
Abstract
Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.
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Affiliation(s)
| | - Tilo Gschwind
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Peter Galer
- Center for Neuroengineering and Therapeutics; Department of Bioengineering; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | | | - Brian Litt
- Center for Neuroengineering and Therapeutics; Department of Bioengineering; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Sándor Beniczky
- Danish Epilepsy Centre Filadelfia, Dianalund, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
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Milne-Ives M, Duun-Henriksen J, Blaabjerg L, Mclean B, Shankar R, Meinert E. At home EEG monitoring technologies for people with epilepsy and intellectual disabilities: A scoping review. Seizure 2023; 110:11-20. [PMID: 37295277 DOI: 10.1016/j.seizure.2023.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/06/2023] [Accepted: 05/07/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Conducting electroencephalography in people with intellectual disabilities (PwID) can be challenging, but the high proportion of PwID who experience seizures make it an essential part of their care. To reduce hospital-based monitoring, interventions are being developed to enable high-quality EEG data to be collected at home. This scoping review aims to summarise the current state of remote EEG monitoring research, potential benefits and limitations of the interventions, and inclusion of PwID in this research. METHODS The review was structured using the PRISMA extension for Scoping Reviews and the PICOS framework. Studies that evaluated a remote EEG monitoring intervention in adults with epilepsy were retrieved from the PubMed, MEDLINE, Embase, CINAHL, Web of Science, and ClinicalTrials.gov databases. A descriptive analysis provided an overview of the study and intervention characteristics, key results, strengths, and limitations. RESULTS 34,127 studies were retrieved and 23 were included. Five types of remote EEG monitoring were identified. Common benefits included producing useful results of comparable quality to inpatient monitoring and patient experience. A common limitation was the challenge of capturing all seizures with a small number of localised electrodes. No randomised controlled trials were included, few studies reported sensitivity and specificity, and only three considered PwID. CONCLUSIONS Overall, the studies demonstrated the feasibility of remote EEG interventions for out-of-hospital monitoring and their potential to improve data collection and quality of care for patients. Further research is needed on the effectiveness, benefits, and limitations of remote EEG monitoring compared to in-patient monitoring, especially for PwID.
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Affiliation(s)
- Madison Milne-Ives
- Centre for Health Technology, University of Plymouth, Plymouth, PL4 6DT, UK
| | | | | | - Brendan Mclean
- Royal Cornwall Hospitals NHS Trust, Treliske, Truro, Cornwall, TR1 3LJ, UK; Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK; Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin, PL31 2QN, UK
| | - Rohit Shankar
- Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK; Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin, PL31 2QN, UK
| | - Edward Meinert
- Centre for Health Technology, University of Plymouth, Plymouth, PL4 6DT, UK; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK; Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, W6 8RP, UK.
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8
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Dan J, Foged MT, Vandendriessche B, Van Paesschen W, Bertrand A. Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG. J Neural Eng 2023; 20. [PMID: 36630712 DOI: 10.1088/1741-2552/acb231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 01/11/2023] [Indexed: 01/12/2023]
Abstract
Objective.The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as 'spikes', in people with epilepsy.Approach.A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen's kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results.Main results.We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise<300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG.Significance.These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.
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Affiliation(s)
- Jonathan Dan
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.,Byteflies, Borsbeeksebrug 22, 2600 Berchem, Belgium
| | - Mette Thrane Foged
- Rigshospitalet, Neurobiology Research Unit, 28 Juliane Maries Vej, DK-2100 Copenhagen, Denmark
| | - Benjamin Vandendriessche
- Byteflies, Borsbeeksebrug 22, 2600 Berchem, Belgium.,Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Wim Van Paesschen
- Department of neurology, UZ Leuven, Herestraat 49, 3000 Leuven, Belgium
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Freund BE, Feyissa AM. EEG as an indispensable tool during and after the COVID-19 pandemic: A review of tribulations and successes. Front Neurol 2022; 13:1087969. [PMID: 36530612 PMCID: PMC9755176 DOI: 10.3389/fneur.2022.1087969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 11/17/2022] [Indexed: 10/03/2023] Open
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, elective and non-emergent tests and procedures were delayed or suspended in lieu of diverting resources to more emergent treatment of critically ill patients and to avoid the spread and contraction of COVID-19. Further, the workforce was stretched thin, and healthcare facilities saw high turnover rates for full-time and contract employees, which strained the system and reduced the ability to provide clinical services. One of the casualties of these changes was electroencephalography (EEG) procedures, which have been performed less frequently throughout the world since the pandemic. Whether considered routine or emergent, the deferral of EEG studies can cause downstream effects, including a delay in diagnosis and initiation of treatment for epilepsy and non-epileptic seizures resulting in a higher risk of morbidity and mortality. Despite these limitations, the importance and utility of EEG and EEG technologists have been reinforced with the development of COVID-related neurological complications, including encephalopathy and seizures, which require EEG for diagnosis and treatment. Since the pandemic, reliance on remote telemonitoring has further highlighted the value and ease of using EEG. There has also been a heightened interest in rapid EEG devices that non-technologist professionals can attach quickly, allowing minimum patient contact to avoid exposure to COVID-19 and taking advantage of remote EEG monitoring. This review discusses the acute and potential long-term effects of the COVID-19 pandemic on the use and performance of EEG.
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Affiliation(s)
| | - Anteneh M. Feyissa
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, United States
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Li W, Wang G, Lei X, Sheng D, Yu T, Wang G. Seizure detection based on wearable devices: A review of device, mechanism, and algorithm. Acta Neurol Scand 2022; 146:723-731. [PMID: 36255131 DOI: 10.1111/ane.13716] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 09/30/2022] [Indexed: 11/30/2022]
Abstract
With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband-type devices, patch-type devices, and armband-type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure-related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.
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Affiliation(s)
- Wen Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Guangming Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiyuan Lei
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Duozheng Sheng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Tao Yu
- Department of Functional Neurosurgery, Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Gang Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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11
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Hanidziar D, Westover MB. Monitoring of sedation in mechanically ventilated patients using remote technology. Curr Opin Crit Care 2022; 28:360-366. [PMID: 35653256 PMCID: PMC9434805 DOI: 10.1097/mcc.0000000000000940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW Two years of coronavirus disease 2019 (COVID-19) pandemic highlighted that excessive sedation in the ICU leading to coma and other adverse outcomes remains pervasive. There is a need to improve monitoring and management of sedation in mechanically ventilated patients. Remote technologies that are based on automated analysis of electroencephalogram (EEG) could enhance standard care and alert clinicians real-time when severe EEG suppression or other abnormal brain states are detected. RECENT FINDINGS High rates of drug-induced coma as well as delirium were found in several large cohorts of mechanically ventilated patients with COVID-19 pneumonia. In patients with acute respiratory distress syndrome, high doses of sedatives comparable to general anesthesia have been commonly administered without defined EEG endpoints. Continuous limited-channel EEG can reveal pathologic brain states such as burst suppression, that cannot be diagnosed by neurological examination alone. Recent studies documented that machine learning-based analysis of continuous EEG signal is feasible and that this approach can identify burst suppression as well as delirium with high specificity. SUMMARY Preventing oversedation in the ICU remains a challenge. Continuous monitoring of EEG activity, automated EEG analysis, and generation of alerts to clinicians may reduce drug-induced coma and potentially improve patient outcomes.
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Affiliation(s)
- Dusan Hanidziar
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA
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12
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Biondi A, Santoro V, Viana PF, Laiou P, Pal DK, Bruno E, Richardson MP. Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review. Epilepsia 2022; 63:1041-1063. [PMID: 35271736 PMCID: PMC9311406 DOI: 10.1111/epi.17220] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/07/2022] [Accepted: 03/07/2022] [Indexed: 11/30/2022]
Abstract
In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.
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Affiliation(s)
- Andrea Biondi
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Viviana Santoro
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Pedro F. Viana
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK,Faculty of MedicineUniversity of LisbonLisbonPortugal
| | - Petroula Laiou
- Department of Biostatistics and Health InformaticsInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Deb K. Pal
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Elisa Bruno
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Mark P. Richardson
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
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