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Yan Y, An X, Ma Y, Jiang Z, Di Y, Li T, Wang H, Ren H, Ma L, Luo B, Huang Y. Detection of early neurological deterioration using a quantitative electroencephalography system in patients with large vessel occlusion stroke after endovascular treatment. J Neurointerv Surg 2024:jnis-2024-022011. [PMID: 39053935 DOI: 10.1136/jnis-2024-022011] [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: 05/19/2024] [Accepted: 07/05/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Early neurological deterioration (END) is a serious complication in patients with large vessel occlusion (LVO) stroke. However, modalities to monitor neurological function after endovascular treatment (EVT) are lacking. This study aimed to evaluate the diagnostic accuracy of a quantitative electroencephalography (qEEG) system for detecting END. METHODS In this prospective, nested case-control study, we included 47 patients with anterior circulation LVO stroke and 34 healthy adults from different clinical centers in Tianjin, China, from May 2023 to January 2024. Patients with stroke underwent EEG at admission and after EVT. The diagnostic accuracy of qEEG features for END was evaluated by receiver operating characteristic curve analysis, and the feasibility was evaluated by the percentage of artifact-free data and device-related adverse events. RESULTS 14 patients with stroke had END (29.8%, 95% CI 16.2% to 43.4%), with most developed within 12 hours of recanalization (n=11). qEEG features showed significant correlations with National Institutes of Health Stroke Scale score and infarct volume. After matching, 13 patients with END and 26 controls were included in the diagnostic analysis. Relative alpha power demonstrated the highest diagnostic accuracy for the affected and unaffected hemispheres. The optimal electrode positions were FC3/4 in the unaffected hemisphere, and F7/8 and C3/4 in the affected hemisphere. No device-related adverse events were reported. CONCLUSION The qEEG system exhibits a high diagnostic accuracy for END and may be a promising tool for monitoring neurological function. The identification of optimal electrode positions may enhance device convenience. CLINICAL TRIAL REGISTRATION ChiCTR 2300070829.
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Affiliation(s)
- Yujia Yan
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, People's Republic of China
| | - Xingwei An
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, People's Republic of China
| | - Yuxiang Ma
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, People's Republic of China
| | - Zeliang Jiang
- Department of Psychology, Hebei Normal University, Shijiazhuang, Hebei, People's Republic of China
| | - Yang Di
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, People's Republic of China
| | - Tingting Li
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, People's Republic of China
| | - Honglin Wang
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, People's Republic of China
| | - Hecheng Ren
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, People's Republic of China
| | - Lin Ma
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, People's Republic of China
| | - Bin Luo
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, People's Republic of China
| | - Ying Huang
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, People's Republic of China
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Peycheva M, Seiler A, Wagner F, Li L, Heldner MR. Journal Club: Prehospital Detection of Large Vessel Occlusion Stroke With Electroencephalography: Results of the ELECTRA-STROKE Study. Neurology 2024; 103:e209587. [PMID: 38870459 DOI: 10.1212/wnl.0000000000209587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024] Open
Abstract
The ELECTRA-STROKE study investigated the potential of EEG for prehospital triage of patients with ischemic stroke due to large vessel occlusion (LVO), in which fast triage to stroke centers for endovascular treatment is crucial. The study was conducted in 4 phases, and this Journal Club article focuses on the fourth phase in the prehospital setting with suspected stroke patients. An EEG cap with dry electrodes was used to measure brain activity. The main focus was on the diagnostic accuracy of the theta/alpha ratio, which yielded an area under the receiver operator characteristic curve (AUC) of 0.80. Secondary endpoints, particularly the Brain Symmetry Index (a quantified EEG interhemispheric cortical power asymmetry index) in the delta frequency band, showed an AUC of 0.91. Despite the convenient study design and user-friendly EEG device, limitations include a single-arm design, a relatively small sample size, and exclusions due to data quality issues. The usefulness of EEG in the detection of neuronal changes based on brain ischemia was highlighted, but uncertainties remain regarding its use in certain patient groups. The improvements in the Brain Symmetry Index from phase 3 to 4 of the study indicate the potential for further refinement and investigation of combined methods to improve diagnostic accuracy. The study provides insight into the role of EEG in prehospital stroke detection, recognizing both the strengths and limitations. Overall, the study contributes to understanding the promise of EEG in optimizing LVO stroke triage and urges further refinement and exploration of complementary diagnostic approaches.
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Affiliation(s)
- Marieta Peycheva
- From the Department of Neurology (M.P.), Medical University of Plovdiv; Research Institute at Medical University of Plovdiv (M.P.), Bulgaria; Department of Neurology (A.S., M.R.H.), and Institute of Diagnostic and Interventional Neuroradiology (F.W.), Inselspital, University Hospital and University of Bern, Switzerland; and Wolfson Centre for the Prevention of Stroke and Dementia (L.L.), Nuffield Department of Clinical Neurosciences, Wolfson Building-John Radcliffe Hospital, University of Oxford, United Kingdom
| | - Andrea Seiler
- From the Department of Neurology (M.P.), Medical University of Plovdiv; Research Institute at Medical University of Plovdiv (M.P.), Bulgaria; Department of Neurology (A.S., M.R.H.), and Institute of Diagnostic and Interventional Neuroradiology (F.W.), Inselspital, University Hospital and University of Bern, Switzerland; and Wolfson Centre for the Prevention of Stroke and Dementia (L.L.), Nuffield Department of Clinical Neurosciences, Wolfson Building-John Radcliffe Hospital, University of Oxford, United Kingdom
| | - Franca Wagner
- From the Department of Neurology (M.P.), Medical University of Plovdiv; Research Institute at Medical University of Plovdiv (M.P.), Bulgaria; Department of Neurology (A.S., M.R.H.), and Institute of Diagnostic and Interventional Neuroradiology (F.W.), Inselspital, University Hospital and University of Bern, Switzerland; and Wolfson Centre for the Prevention of Stroke and Dementia (L.L.), Nuffield Department of Clinical Neurosciences, Wolfson Building-John Radcliffe Hospital, University of Oxford, United Kingdom
| | - Linxin Li
- From the Department of Neurology (M.P.), Medical University of Plovdiv; Research Institute at Medical University of Plovdiv (M.P.), Bulgaria; Department of Neurology (A.S., M.R.H.), and Institute of Diagnostic and Interventional Neuroradiology (F.W.), Inselspital, University Hospital and University of Bern, Switzerland; and Wolfson Centre for the Prevention of Stroke and Dementia (L.L.), Nuffield Department of Clinical Neurosciences, Wolfson Building-John Radcliffe Hospital, University of Oxford, United Kingdom
| | - Mirjam R Heldner
- From the Department of Neurology (M.P.), Medical University of Plovdiv; Research Institute at Medical University of Plovdiv (M.P.), Bulgaria; Department of Neurology (A.S., M.R.H.), and Institute of Diagnostic and Interventional Neuroradiology (F.W.), Inselspital, University Hospital and University of Bern, Switzerland; and Wolfson Centre for the Prevention of Stroke and Dementia (L.L.), Nuffield Department of Clinical Neurosciences, Wolfson Building-John Radcliffe Hospital, University of Oxford, United Kingdom
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Tong W, Yue W, Chen F, Shi W, Zhang L, Wan J. MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection. SENSORS (BASEL, SWITZERLAND) 2024; 24:4234. [PMID: 39001013 PMCID: PMC11244239 DOI: 10.3390/s24134234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/12/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.
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Affiliation(s)
- Wei Tong
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Weiqi Yue
- School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Fangni Chen
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Wei Shi
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Lei Zhang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Jian Wan
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
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Peterson W, Ramakrishnan N, Browder K, Sanossian N, Nguyen P, Fink E. Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods. J Stroke Cerebrovasc Dis 2024; 33:107714. [PMID: 38636829 DOI: 10.1016/j.jstrokecerebrovasdis.2024.107714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/15/2024] [Accepted: 04/06/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVES We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed. MATERIALS AND METHODS Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke). RESULTS Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an AUC of 0.95, with a sensitivity and specificity of 93% and 86%, respectively. Allowing for multiple recordings per subject in the training set boosted sensitivity by 7%, attributable to a more balanced dataset. CONCLUSIONS Our work demonstrates strong potential for the use of EEG in conjunction with machine learning methods to distinguish stroke patients from healthy subjects. Our approach provides a solution that is not only timely (3-minutes recording time) but also highly precise and accurate (AUC: 0.95).
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Affiliation(s)
| | | | | | - Nerses Sanossian
- Roxanna Todd Hodges Stroke Program, United States; Keck School of Medicine of the University of Southern California, United States
| | - Peggy Nguyen
- Keck School of Medicine of the University of Southern California, United States
| | - Ezekiel Fink
- Houston Hospital, Houston, TX, United States; Weill Cornell School of Medicine Sciences, New York, NY, United States
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van den Hoek TC, van de Ruit M, Terwindt GM, Tolner EA. EEG Changes in Migraine-Can EEG Help to Monitor Attack Susceptibility? Brain Sci 2024; 14:508. [PMID: 38790486 PMCID: PMC11119734 DOI: 10.3390/brainsci14050508] [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: 04/03/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/26/2024] Open
Abstract
Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack of reliable predictors for upcoming attacks, which may help in understanding pathophysiological mechanisms to identify new treatment targets that may be positioned between the acute and preventive possibilities that are currently available. So far, a large range of studies using conventional hospital-based EEG recordings have provided contradictory results, with indications of both cortical hyper- as well as hypo-excitability. These heterogeneous findings may largely be because most studies were cross-sectional in design, providing only a snapshot in time of a patient's brain state without capturing day-to-day fluctuations. The scope of this narrative review is to (i) reflect on current knowledge on EEG changes in the context of migraine, the attack cycle, and underlying pathophysiology; (ii) consider the effects of migraine treatment on EEG features; (iii) outline challenges and opportunities in using EEG for monitoring attack susceptibility; and (iv) discuss future applications of EEG in home-based settings.
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Affiliation(s)
- Thomas C. van den Hoek
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
| | - Mark van de Ruit
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
- Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Gisela M. Terwindt
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
| | - Else A. Tolner
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
- Department of Human Genetics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
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Favilla CG, Baird GL, Grama K, Konecky S, Carter S, Smith W, Gitlevich R, Lebron-Cruz A, Yodh AG, McTaggart RA. Portable cerebral blood flow monitor to detect large vessel occlusion in patients with suspected stroke. J Neurointerv Surg 2024:jnis-2024-021536. [PMID: 38514189 DOI: 10.1136/jnis-2024-021536] [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: 01/25/2024] [Accepted: 03/10/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Early detection of large vessel occlusion (LVO) facilitates triage to an appropriate stroke center to reduce treatment times and improve outcomes. Prehospital stroke scales are not sufficiently sensitive, so we investigated the ability of the portable Openwater optical blood flow monitor to detect LVO. METHODS Patients were prospectively enrolled at two comprehensive stroke centers during stroke alert evaluation within 24 hours of onset with National Institutes of Health Stroke Scale (NIHSS) score ≥2. A 70 s bedside optical blood flow scan generated cerebral blood flow waveforms based on relative changes in speckle contrast. Anterior circulation LVO was determined by CT angiography. A deep learning model trained on all patient data using fivefold cross-validation and learned discriminative representations from the raw speckle contrast waveform data. Receiver operating characteristic (ROC) analysis compared the Openwater diagnostic performance (ie, LVO detection) with prehospital stroke scales. RESULTS Among 135 patients, 52 (39%) had an anterior circulation LVO. The median NIHSS score was 8 (IQR 4-14). The Openwater instrument had 79% sensitivity and 84% specificity for the detection of LVO. The rapid arterial occlusion evaluation (RACE) scale had 60% sensitivity and 81% specificity and the Los Angeles motor scale (LAMS) had 50% sensitivity and 81% specificity. The binary Openwater classification (high-likelihood vs low-likelihood) had an area under the ROC (AUROC) of 0.82 (95% CI 0.75 to 0.88), which outperformed RACE (AUC 0.70; 95% CI 0.62 to 0.78; P=0.04) and LAMS (AUC 0.65; 95% CI 0.57 to 0.73; P=0.002). CONCLUSIONS The Openwater optical blood flow monitor outperformed prehospital stroke scales for the detection of LVO in patients undergoing acute stroke evaluation in the emergency department. These encouraging findings need to be validated in an independent test set and the prehospital environment.
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Affiliation(s)
- Christopher G Favilla
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Grayson L Baird
- Department of Interventional Radiology, Brown University, Providence, Rhode Island, USA
| | | | | | - Sarah Carter
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Wendy Smith
- Department of Diagnostic Imaging, Lifespan Health System, Providence, Rhode Island, USA
| | - Rebecca Gitlevich
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alexa Lebron-Cruz
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Arjun G Yodh
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ryan A McTaggart
- Department of Interventional Radiology, Brown University, Providence, Rhode Island, USA
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van Stigt MN, Groenendijk EA, van Meenen LCC, van de Munckhof AAGA, Theunissen M, Franschman G, Smeekes MD, van Grondelle JAF, Geuzebroek G, Siegers A, Visser MC, van Schaik SM, Halkes PHA, Majoie CBLM, Roos YBWEM, Koelman JHTM, Koopman MS, Marquering HA, Potters WV, Coutinho JM. Prehospital Detection of Large Vessel Occlusion Stroke With EEG. Neurology 2023; 101:e2522-e2532. [PMID: 37848336 PMCID: PMC10791060 DOI: 10.1212/wnl.0000000000207831] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/31/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Endovascular thrombectomy (EVT) is standard treatment for anterior large vessel occlusion stroke (LVO-a stroke). Prehospital diagnosis of LVO-a stroke would reduce time to EVT by allowing direct transportation to an EVT-capable hospital. We aim to evaluate the diagnostic accuracy of dry electrode EEG for the detection of LVO-a stroke in the prehospital setting. METHODS ELECTRA-STROKE was an investigator-initiated, prospective, multicenter, diagnostic study, performed in the prehospital setting. Adult patients were eligible if they had suspected stroke (as assessed by the attending ambulance nurse) and symptom onset <24 hours. A single dry electrode EEG recording (8 electrodes) was performed by ambulance personnel. Primary endpoint was the diagnostic accuracy of the theta/alpha frequency ratio for LVO-a stroke (intracranial ICA, A1, M1, or proximal M2 occlusion) detection among patients with EEG data of sufficient quality, expressed as the area under the receiver operating characteristic curve (AUC). Secondary endpoints were diagnostic accuracies of other EEG features quantifying frequency band power and the pairwise derived Brain Symmetry Index. Neuroimaging was assessed by a neuroradiologist blinded to EEG results. RESULTS Between August 2020 and September 2022, 311 patients were included. The median EEG duration time was 151 (interquartile range [IQR] 151-152) seconds. For 212/311 (68%) patients, EEG data were of sufficient quality for analysis. The median age was 74 (IQR 66-81) years, 90/212 (42%) were women, and the median baseline NIH Stroke Scale was 1 (IQR 0-4). Six (3%) patients had an LVO-a stroke, 109/212 (51%) had a non-LVO-a ischemic stroke, 32/212 (15%) had a transient ischemic attack, 8/212 (4%) had a hemorrhagic stroke, and 57/212 (27%) had a stroke mimic. AUC of the theta/alpha ratio was 0.80 (95% CI 0.58-1.00). Of the secondary endpoints, the pairwise derived Brain Symmetry Index in the delta frequency band had the highest diagnostic accuracy (AUC 0.91 [95% CI 0.73-1.00], sensitivity 80% [95% CI 38%-96%], specificity 93% [95% CI 88%-96%], positive likelihood ratio 11.0 [95% CI 5.5-21.7]). DISCUSSION The data from this study suggest that dry electrode EEG has the potential to detect LVO-a stroke among patients with suspected stroke in the prehospital setting. Toward future implementation of EEG in prehospital stroke care, EEG data quality needs to be improved. TRIAL REGISTRATION INFORMATION ClinicalTrials.gov identifier: NCT03699397. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that prehospital dry electrode scalp EEG accurately detects LVO-a stroke among patients with suspected acute stroke.
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Affiliation(s)
- Maritta N van Stigt
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Eva A Groenendijk
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Laura C C van Meenen
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Anita A G A van de Munckhof
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Monique Theunissen
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Gaby Franschman
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Martin D Smeekes
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Joffry A F van Grondelle
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Geertje Geuzebroek
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Arjen Siegers
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Marieke C Visser
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Sander M van Schaik
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Patricia H A Halkes
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Charles B L M Majoie
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Yvo B W E M Roos
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Johannes H T M Koelman
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Miou S Koopman
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Henk A Marquering
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Wouter V Potters
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
| | - Jonathan M Coutinho
- From the Departments of Clinical Neurophysiology (M.N.v.S., E.A.G., J.H.T.M.K.), Neurology (M.N.v.S., E.A.G., L.C.C.v.M., A.A.G.A.v.d.M., M.C.V., Y.B.W.E.M.R., J.M.C.), Radiology and Nuclear Medicine (C.B.L.M.M., M.S.K., H.A.M.), and Biomedical Engineering and Physics (H.A.M.), Amsterdam UMC location University of Amsterdam; Witte Kruis Ambulancezorg (M.T., G.F.), Alkmaar; Ambulancezorg Nederland (M.D.S.), Zwolle; Ambulance Amsterdam (J.A.F.v.G., G.G., A.S.); Department of Neurology (S.M.v.S.), OLVG Hospital location West, Amsterdam; Department of Neurology (P.H.A.H.), Noordwest Ziekenhuisgroep location Alkmaar; TrianecT (W.V.P.), Utrecht, the Netherlands
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Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Int J Med Inform 2023; 180:105274. [PMID: 37944275 DOI: 10.1016/j.ijmedinf.2023.105274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023]
Abstract
Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies. METHODS The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms. FINDINGS AND DISCUSSION The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction. CONCLUSION Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.
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Affiliation(s)
| | - Sven Tomforde
- Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany
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9
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Groenendijk EA, van Stigt MN, van de Munckhof AAGA, Koelman JHTM, Koopman MS, Marquering HA, Potters WV, Coutinho JM. Subhairline Electroencephalography for the Detection of Large Vessel Occlusion Stroke. J Am Heart Assoc 2023; 12:e031929. [PMID: 37982212 PMCID: PMC10727307 DOI: 10.1161/jaha.123.031929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/28/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Endovascular thrombectomy is standard treatment for patients with anterior circulation large vessel occlusion stroke (LVO-a). Prehospital identification of these patients would enable direct routing to an endovascular thrombectomy-capable hospital and consequently reduce time-to-endovascular thrombectomy. Electroencephalography (EEG) has previously proven to be promising for LVO-a stroke detection. Fast and reliable electrode application, however, can remain a challenge. A potential alternative is subhairline EEG. We evaluated the diagnostic accuracy of subhairline EEG for LVO-a stroke detection. METHODS AND RESULTS We included adult patients with a suspected stroke or known LVO-a stroke and symptom onset time <24 hours. A single 3-minute EEG recording was performed at the emergency department, before endovascular thrombectomy, using 9 self-adhesive electrodes placed on the forehead and behind the ears. We evaluated the diagnostic accuracies of EEG features quantifying frequency band power and brain symmetry (pairwise derived Brain Symmetry Index) for LVO-a stroke detection using receiver operating characteristic analysis. EEG data were of sufficient quality for analysis in 51/52 (98%) included patients. Of these patients, 16 (31%) had an LVO-a stroke, 16 (31%) a non-LVO-a ischemic stroke, 5 (10%) a transient ischemic attack, and 14 (27%) a stroke mimic. Median symptom-onset-to-EEG-time was 266 (interquartile range 130-709) minutes. The highest diagnostic accuracy for LVO-a stroke detection was reached by the pairwise derived Brain Symmetry Index in the theta frequency band (area under the receiver operating characteristic curve 0.90; sensitivity 86%; specificity 83%). CONCLUSIONS Subhairline EEG could detect LVO-a stroke with high diagnostic accuracy and had high data reliability. These data suggest that subhairline EEG is potentially suitable as a prehospital stroke triage instrument.
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Affiliation(s)
- Eva A. Groenendijk
- Department of Clinical NeurophysiologyAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
- Department of NeurologyAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | - Maritta N. van Stigt
- Department of Clinical NeurophysiologyAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
- Department of NeurologyAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | | | - Johannes H. T. M. Koelman
- Department of Clinical NeurophysiologyAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | - Miou S. Koopman
- Department of Radiology and Nuclear MedicineAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | - Henk A. Marquering
- Department of Radiology and Nuclear MedicineAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
- Department of Biomedical Engineering and PhysicsAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | | | - Jonathan M. Coutinho
- Department of NeurologyAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
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10
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Warsito IF, Komosar M, Bernhard MA, Fiedler P, Haueisen J. Flower electrodes for comfortable dry electroencephalography. Sci Rep 2023; 13:16589. [PMID: 37789022 PMCID: PMC10547758 DOI: 10.1038/s41598-023-42732-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/14/2023] [Indexed: 10/05/2023] Open
Abstract
Dry electroencephalography (EEG) electrodes provide rapid, gel-free, and easy EEG preparation, but with limited wearing comfort. We propose a novel dry electrode comprising multiple tilted pins in a flower-like arrangement. The novel Flower electrode increases wearing comfort and contact area while maintaining ease of use. In a study with 20 volunteers, we compare the performance of a novel 64-channel dry Flower electrode cap to a commercial dry Multipin electrode cap in sitting and supine positions. The wearing comfort of the Flower cap was rated as significantly improved both in sitting and supine positions. The channel reliability and average impedances of both electrode systems were comparable. Averaged VEP components showed no considerable differences in global field power amplitude and latency, as well as in signal-to-noise ratio and topography. No considerable differences were found in the power spectral density of the resting state EEGs between 1 and 40 Hz. Overall, our findings provide evidence for equivalent channel reliability and signal characteristics of the compared cap systems in the sitting and supine positions. The reliability, signal quality, and significantly improved wearing comfort of the Flower electrode allow new fields of applications for dry EEG in long-term monitoring, sensitive populations, and recording in supine position.
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Affiliation(s)
- Indhika Fauzhan Warsito
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany
| | - Milana Komosar
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany
| | - Maria Anne Bernhard
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany.
- Department of Neurology, Biomagnetic Center, University Hospital Jena, Jena, Germany.
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11
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Guo X, Dye J. Modern Prehospital Screening Technology for Emergent Neurovascular Disorders. Adv Biol (Weinh) 2023; 7:e2300174. [PMID: 37357150 DOI: 10.1002/adbi.202300174] [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: 05/05/2023] [Revised: 05/14/2023] [Indexed: 06/27/2023]
Abstract
Stroke is a serious neurological disease and a significant contributor to disability worldwide. Traditional in-hospital imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) remain the standard modalities for diagnosing stroke. The development of prehospital stroke detection devices may facilitate earlier diagnosis, initiation of stroke care, and ultimately better patient outcomes. In this review, the authors summarize the features of eight stroke detection devices using noninvasive brain scanning technology. The review summarizes the features of stroke detection devices including portable CT, MRI, transcranial Doppler ultrasound , microwave tomographic imaging, electroencephalography, near-infrared spectroscopy, volumetric impedance phaseshift spectroscopy, and cranial accelerometry. The technologies utilized, the indications for application, the environments indicated for application, the physical features of the eight stroke detection devices, and current commercial products are discussed. As technology advances, multiple portable stroke detection instruments exhibit the promising potential to expedite the diagnosis of stroke and enhance the time taken for treatment, ultimately aiding in prehospital stroke triage.
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Affiliation(s)
- Xiaofan Guo
- Department of Neurology, Loma Linda University, Loma Linda, CA, 92354, USA
| | - Justin Dye
- Department of Neurosurgery, Loma Linda University, Loma Linda, CA, 92354, USA
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12
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van Stigt MN, Groenendijk EA, van de Munckhof AAGA, Marquering HA, Koopman MS, Majoie CBLM, Roos YBWEM, Koelman JHTM, Potters WV, Coutinho JM. Correlation between EEG spectral power and cerebral perfusion in patients with acute ischemic stroke. J Clin Neurosci 2023; 116:81-86. [PMID: 37657169 DOI: 10.1016/j.jocn.2023.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 08/08/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023]
Abstract
Dry electrode electroencephalography (EEG) has the potential to diagnose ischemic stroke in the acute phase. In the current study we determined the correlation between EEG spectral power and ischemic stroke size and location as determined by computed tomography perfusion (CTP). Dry electrode EEG recordings were performed in patients with acute ischemic stroke in the emergency room. CTP preceded the EEG recordings as part of standard imaging protocol. Infarct core volume, total hypoperfused volume and local cerebral blood flow (CBF) were estimated with CTP. Additionally, global and local EEG spectral power were determined. We used Spearman's correlation coefficients to evaluate the correlation between variables. We included 27 patients (median age 72 [IQR:69-80] years, 15/27 [56%] men). Median CTP-to-EEG time was 32 (range:8-138) minutes. Hypoperfused volumes were estimated for 12/27 (44%) patients. Infarct core volume correlated best with global delta power (ρ = 0.76, p < 0.01), total hypoperfused volume with global alpha power (ρ = -0.58, p = 0.05), and local CBF with local alpha power (ρ = 0.43, p < 0.01). We conclude that dry electrode EEG signals slow down with increasing hypoperfused volume, which could potentially be used to discriminate between small and large ischemic strokes.
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Affiliation(s)
- M N van Stigt
- Department of Clinical Neurophysiology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Department of Neurology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands.
| | - E A Groenendijk
- Department of Clinical Neurophysiology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Department of Neurology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - A A G A van de Munckhof
- Department of Clinical Neurophysiology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Department of Neurology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - H A Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - M S Koopman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - C B L M Majoie
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Y B W E M Roos
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - J H T M Koelman
- Department of Clinical Neurophysiology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Department of Neurology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - W V Potters
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; TrianecT, Padualaan 8, Utrecht, the Netherlands
| | - J M Coutinho
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
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Fassbender K, Lesmeister M, Merzou F. Prehospital stroke management and mobile stroke units. Curr Opin Neurol 2023; 36:140-146. [PMID: 36794965 PMCID: PMC9994848 DOI: 10.1097/wco.0000000000001150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
PURPOSE OF REVIEW Delayed presentation at the hospital contributes to poorer patient outcomes and undertreatment of acute stroke patients. This review will discuss recent developments in prehospital stroke management and mobile stroke units aimed to improve timely access to treatment within the past 2 years and will point towards future directions. RECENT FINDINGS Recent progress in research into prehospital stroke management and mobile stroke units ranges from interventions aimed at improving patients' help-seeking behaviour, to the education of emergency medical services team members, to the use of innovative referral methods, such as diagnostic scales, and finally to evidence of improved outcomes by the use of mobile stroke units. SUMMARY Understanding is increasing about the need for optimizing stroke management over the entire stroke rescue chain with the goal of improving access to highly effective time-sensitive treatment. In the future, we can expect that novel digital technologies and artificial intelligence will become relevant in effective interaction between prehospital and in-hospital stroke-treating teams, with beneficial effects on patients' outcomes.
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Affiliation(s)
- Klaus Fassbender
- Department of Neurology, Saarland University Medical Center, Homburg, Germany
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Barton DJ, Coppler PJ, Talia NN, Charalambides A, Stancil B, Puccio AM, Okonkwo DO, Callaway CW, Guyette FX, Elmer J. Prehospital Electroencephalography to Detect Traumatic Brain Injury during Helicopter Transport: A Pilot Observational Cohort Study. PREHOSP EMERG CARE 2023; 28:405-412. [PMID: 36857200 PMCID: PMC10497709 DOI: 10.1080/10903127.2023.2185333] [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: 11/21/2022] [Revised: 01/09/2023] [Accepted: 02/21/2023] [Indexed: 03/02/2023]
Abstract
OBJECTIVE Early recognition of traumatic brain injury (TBI) is important to facilitate time-sensitive care. Electroencephalography (EEG) can identify TBI, but feasibility of EEG has not been evaluated in prehospital settings. We tested the feasibility of obtaining single-channel EEG during air medical transport after trauma. We measured association between quantitative EEG features, early blood biomarkers, and abnormalities on head computerized tomography (CT). METHODS We performed a pilot prospective, observational study enrolling consecutive patients transported by critical care air ambulance from the scene of trauma to a Level I trauma center. During transport, prehospital clinicians placed a sensor on the patient's forehead to record EEG. We reviewed EEG waveforms and selected 90 seconds of recording for quantitative analysis. EEG data processing included fast Fourier transform to summarize component frequency power in the delta (0-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) ranges. We collected blood samples on day 1 and day 3 post-injury and measured plasma levels of two brain injury biomarkers (ubiquitin C-terminal hydrolase L1 [UCH-L1] and glial fibrillary acidic protein [GFAP]). We compared predictors between individuals with and without CT-positive TBI findings. RESULTS Forty subjects were enrolled, with EEG recordings successfully obtained in 34 (85%). Reasons for failure included uncharged battery (n = 5) and user error (n = 1). Data were lost in three cases. Of 31 subjects with data, interpretable EEG signal was recorded in 26 (84%). Mean age was 48 (SD 16) years, 79% were male, and 50% suffered motor vehicle crashes. Eight subjects (24%) had CT-positive TBI. Subjects with and without CT-positive TBI had similar median delta power, alpha power, and theta power. UCH-L1 and GFAP plasma levels did not differ across groups. Delta power inversely correlated with UCH-L1 day 1 plasma concentration (r = -0.60, p = 0.03). CONCLUSIONS Prehospital EEG acquisition is feasible during air transport after trauma.
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Affiliation(s)
- David J. Barton
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Patrick J. Coppler
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Nadine N. Talia
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA
| | | | | | - Ava M. Puccio
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA
| | - David O. Okonkwo
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA
| | | | - Francis X. Guyette
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Jonathan Elmer
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA
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15
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Agrawal D, Dhillon P, Siow I, Lee KS, Spooner O, Yeo L, Bhogal P. Prehospital technologies for early stroke detection - A review. Interv Neuroradiol 2023:15910199231152372. [PMID: 36654460 DOI: 10.1177/15910199231152372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
The rate of neural circuitry loss in a typical large vessel occlusion well emphasizes that 'Time is Brain'. Every untreated minute in a large vessel ischaemic stroke results in loss of 1.9 million neurons and 13.8 billion synapses. As such, it is essential to optimize the flow-limiting steps in delivering the current standard of care. The current diagnostic model involves recognition of symptoms by patients, followed by access to Emergency Medical Services and subsequent physical examination and neuroimaging in the Emergency Department. With more than 50% of stroke patients using Emergency Medical Services as the first point of care contact, it can be deduced that the outcome of the 'stroke chain of survival' can be improved by addressing the bottleneck of prehospital stroke diagnosis. Here we present a review of the existing technologies.
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Affiliation(s)
- Deepsha Agrawal
- Department of Radiology, 6397Oxford University Hospitals NHS Trust, Oxford, UK
| | - Permesh Dhillon
- Department of Interventional Neuroradiology, 105590Queens Medical Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Isabel Siow
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Oliver Spooner
- Department of Stroke Medicine, 112001The Royal London Hospital, Barts NHS Trust, London, UK
| | - Leonard Yeo
- Division of Neurology, Department of Medicine, National University Health System, Singapore
| | - Pervinder Bhogal
- Department of Interventional Neuroradiology, 112001The Royal London Hospital, Barts NHS Trust, London, UK
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16
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Ng CR, Fiedler P, Kuhlmann L, Liley D, Vasconcelos B, Fonseca C, Tamburro G, Comani S, Lui TKY, Tse CY, Warsito IF, Supriyanto E, Haueisen J. Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22208079. [PMID: 36298430 PMCID: PMC9612204 DOI: 10.3390/s22208079] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/13/2022] [Accepted: 10/19/2022] [Indexed: 05/27/2023]
Abstract
Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.
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Affiliation(s)
- Chuen Rue Ng
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany
| | - Levin Kuhlmann
- Faculty of Information Technology, Monash University, Building 63, 25 Exhibition Walk, Clayton, VIC 3800, Australia
| | - David Liley
- Brain and Psychological Sciences Research Centre, Swinburne University of Technology, P.O. Box 218, Hawthorn, VIC 3122, Australia
| | - Beatriz Vasconcelos
- Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal
| | - Carlos Fonseca
- Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal
- Institute of Science and Innovation in Mechanical and Industrial Engineering, LAETA/INEGI, 4200-465 Porto, Portugal
| | - Gabriella Tamburro
- BIND-Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy
| | - Silvia Comani
- BIND-Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy
| | - Troby Ka-Yan Lui
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Marie-Curie-Straße, 23562 Lübeck, Germany
| | - Chun-Yu Tse
- Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China
| | - Indhika Fauzhan Warsito
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany
| | - Eko Supriyanto
- IJN-UTM Cardiovascular Engineering Centre, School of Biomedical Engineering & Health Sciences, Universiti Teknologi Malaysia, Johor Bahru 81300, Malaysia
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany
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van Stigt MN, van de Munckhof AAGA, van Meenen LCC, Groenendijk EA, Theunissen M, Franschman G, Smeekes MD, van Grondelle JAF, Geuzebroek G, Siegers A, Marquering HA, Majoie CBLM, Roos YBWEM, Koelman JHTM, Potters WV, Coutinho JM. ELECTRA-STROKE: Electroencephalography controlled triage in the ambulance for acute ischemic stroke—Study protocol for a diagnostic trial. Front Neurol 2022; 13:1018493. [PMID: 36262832 PMCID: PMC9576201 DOI: 10.3389/fneur.2022.1018493] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background Endovascular thrombectomy (EVT) is the standard treatment for large vessel occlusion stroke of the anterior circulation (LVO-a stroke). Approximately half of EVT-eligible patients are initially presented to hospitals that do not offer EVT. Subsequent inter-hospital transfer delays treatment, which negatively affects patients' prognosis. Prehospital identification of patients with LVO-a stroke would allow direct transportation of these patients to an EVT-capable center. Electroencephalography (EEG) may be suitable for this purpose because of its sensitivity to cerebral ischemia. The hypothesis of ELECTRA-STROKE is that dry electrode EEG is feasible for prehospital detection of LVO-a stroke. Methods ELECTRA-STROKE is an investigator-initiated, diagnostic study. EEG recordings will be performed in patients with a suspected stroke in the ambulance. The primary endpoint is the diagnostic accuracy of the theta/alpha ratio for the diagnosis of LVO-a stroke, expressed by the area under the receiver operating characteristic (ROC) curve. EEG recordings will be performed in 386 patients. Discussion If EEG can be used to identify LVO-a stroke patients with sufficiently high diagnostic accuracy, it may enable direct routing of these patients to an EVT-capable center, thereby reducing time-to-treatment and improving patient outcomes. Clinical trial registration ClinicalTrials.gov, identifier: NCT03699397.
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Affiliation(s)
- Maritta N. van Stigt
- Department of Clinical Neurophysiology, Amsterdam University Medical Centers (UMC) Location University of Amsterdam, Amsterdam, Netherlands
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
| | - Anita A. G. A. van de Munckhof
- Department of Clinical Neurophysiology, Amsterdam University Medical Centers (UMC) Location University of Amsterdam, Amsterdam, Netherlands
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
| | - Laura C. C. van Meenen
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
| | - Eva A. Groenendijk
- Department of Clinical Neurophysiology, Amsterdam University Medical Centers (UMC) Location University of Amsterdam, Amsterdam, Netherlands
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
| | | | | | | | | | | | | | - Henk A. Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
| | - Charles B. L. M. Majoie
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
| | - Yvo B. W. E. M. Roos
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
| | - Johannes H. T. M. Koelman
- Department of Clinical Neurophysiology, Amsterdam University Medical Centers (UMC) Location University of Amsterdam, Amsterdam, Netherlands
| | - Wouter V. Potters
- Department of Clinical Neurophysiology, Amsterdam University Medical Centers (UMC) Location University of Amsterdam, Amsterdam, Netherlands
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
| | - Jonathan M. Coutinho
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands
- *Correspondence: Jonathan M. Coutinho
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Jin X, Li P, Michalski D, Li S, Zhang Y, Jolkkonen J, Cui L, Didwischus N, Xuan W, Boltze J. Perioperative stroke: A perspective on challenges and opportunities for experimental treatment and diagnostic strategies. CNS Neurosci Ther 2022; 28:497-509. [PMID: 35224865 PMCID: PMC8928912 DOI: 10.1111/cns.13816] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 02/06/2023] Open
Abstract
Perioperative stroke is an ischemic or hemorrhagic cerebral event during or up to 30 days after surgery. It is a feared condition due to a relatively high incidence, difficulties in timely detection, and unfavorable outcome compared to spontaneously occurring stroke. Recent preclinical data suggest that specific pathophysiological mechanisms such as aggravated neuroinflammation contribute to the detrimental impact of perioperative stroke. Conventional treatment options are limited in the perioperative setting due to difficult diagnosis and medications affecting coagulation in may cases. On the contrary, the chance to anticipate cerebrovascular events at the time of surgery may pave the way for prevention strategies. This review provides an overview on perioperative stroke incidence, related problems, and underlying pathophysiological mechanisms. Based on this analysis, we assess experimental stroke treatments including neuroprotective approaches, cell therapies, and conditioning medicine strategies regarding their potential use in perioperative stroke. Interestingly, the specific aspects of perioperative stroke might enable a more effective application of experimental treatment strategies such as classical neuroprotection whereas others including cell therapies may be of limited use. We also discuss experimental diagnostic options for perioperative stroke augmenting classical clinical and imaging stroke diagnosis. While some experimental stroke treatments may have specific advantages in perioperative stroke, the paucity of established guidelines or multicenter clinical research initiatives currently limits their thorough investigation.
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Affiliation(s)
- Xia Jin
- Department of Anesthesiology, Renji Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Peiying Li
- Department of Anesthesiology, Renji Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | | | - Shen Li
- Department of Neurology and Psychiatry, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Yueman Zhang
- Department of Anesthesiology, Renji Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Jukka Jolkkonen
- Department of Neurology and A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Lili Cui
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Nadine Didwischus
- School of Life Sciences, University of Warwick, Coventry, UK.,Department of Radiology, University of Pittsburgh, Pittsburgh, USA.,McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, USA
| | - Wei Xuan
- Department of Anesthesiology, Renji Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Johannes Boltze
- School of Life Sciences, University of Warwick, Coventry, UK
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