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Yayıcı Köken Ö, Şekeroğlu B, Şanlıdağ B, Sarı Yanartaş M, Yılmaz A. Focality in childhood absence epilepsy. Neurol Res 2024; 46:626-633. [PMID: 38643974 DOI: 10.1080/01616412.2024.2339114] [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: 10/06/2023] [Accepted: 03/31/2024] [Indexed: 04/23/2024]
Abstract
BACKGROUND AND PURPOSE Childhood absence epilepsy (CAE) has a typical electroencephalography (EEG) pattern of generalized 3 Hz spike and wave discharges (SWD). Focal interictal discharges were also documented in a small number of documents. The aim was to investigate the amplitudes of interictal 3 Hz SWD within the 1st second in drug-naïve CAE patients. In this way, areas with maximal electronegativity at the beginning of clinically generalized discharges will be documented. METHODS The EEG records of children with drug-naïve CAE were evaluated retrospectively by two child neurologists first for 3 Hz SWD. Then, a machine-learning model evaluated the amplitudes of 3 Hz in the 1st second of SWD. Maximum electronegativity areas were documented and classified as focal, bilateral, and generalized. RESULTS One hundred and twelve 3 Hz SWD were evaluated in 11 patients. Among discharges within the 1st second, maximum electronegativity areas were documented as focal for 44 (39.2%), bilateral for 8 (7.1%), generalized for 60 (53.5%). Among focal electronegativity areas, mostly right central, left occipital and midline parietal areas were documented in 12 (10.7%), 7 (6.2%), and 6 (5.3%), respectively. Eight (7.1%) of the maximum electronegativity areas were detected bilaterally, of which 7 (6.2%) originated from the frontopolar areas. CONCLUSIONS Focal maximal electronegativity areas were frequently observed in drug-naïve CAE patients, comprising approximately half of non-generalized discharges. Focal discharges might be misleading in diagnosis. Focal areas within the brain may be responsible for and contribute to absence seizures that appear bilaterally symmetrical and generalized clinically. Although its clinical implication is unknown, this warrants further study.
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Affiliation(s)
- Özlem Yayıcı Köken
- Faculty of Medicine, Department of Pediatric Neurology, Akdeniz University, Antalya, Turkey
| | - Boran Şekeroğlu
- Artificial Intelligence Engineering, Near East University, Nicosia, Cyprus
- DESAM Institute, Near East University, Nicosia, Cyprus
| | - Burçin Şanlıdağ
- Faculty of Medicine, Department of Pediatric Neurology, Near East University, Nicosia, Cyprus
| | - Mehpare Sarı Yanartaş
- Faculty of Medicine, Department of Pediatric Neurology, Akdeniz University, Antalya, Turkey
| | - Arzu Yılmaz
- Ministry of Health, Ankara Research and Training Hospital, Department of Pediatric Neurology, Ankara, Turkey
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Liu G, Wang Y, Tian F, Chen W, Cui L, Jiang M, Zhang Y, Gao K, Su Y, Wang H. Quantitative EEG reactivity induced by electrical stimulation predicts good outcome in comatose patients after cardiac arrest. Ann Intensive Care 2024; 14:99. [PMID: 38935167 PMCID: PMC11211292 DOI: 10.1186/s13613-024-01339-6] [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: 04/06/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND EEG reactivity is a predictor for neurological outcome in comatose patients after cardiac arrest (CA); however, its application is limited by variability in stimulus types and visual assessment. We aimed to evaluate the prognostic value of the quantitative analysis of EEG reactivity induced by standardized electrical stimulation and for early prognostication in this population. METHODS This prospective observational study recruited post-CA comatose patients in Xuanwu Hospital, Capital Medical University (Beijing, China) between January 2016 and June 2023. EEG reactivity to electrical or traditional pain stimulation was randomly performed via visual and quantitative analysis. Neurological outcome within 6 months was dichotomized as good (Cerebral Performance Categories, CPC 1-2) or poor (CPC 3-5). RESULTS Fifty-eight post-CA comatose patients were admitted, and 52 patients were included in the final analysis, of which 19 (36.5%) had good outcomes. EEG reactivity induced with the electrical stimulation had superior performance to the traditional pain stimulation for good outcome prediction (quantitative analysis: AUC 0.932 vs. 0.849, p = 0.048). When using the electrical stimulation, the AUC of EEG reactivity to predict good outcome by visual analysis was 0.838, increasing to 0.932 by quantitative analysis (p = 0.039). Comparing to the traditional pain stimulation by visual analysis, the AUC of EEG reactivity for good prognostication by the electrical stimulation with quantitative analysis was significantly improved (0.932 vs. 0.770, p = 0.004). CONCLUSIONS EEG reactivity induced by the standardized electrical stimulation in combination with quantitative analysis is a promising formula for post-CA comatose patients, with increased predictive accuracy.
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Affiliation(s)
- Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Yuan Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Fei Tian
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Weibi Chen
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Lili Cui
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Mengdi Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Yan Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Keming Gao
- Department of Psychiatry, Mood Disorders Program, University Hospitals Cleveland Medical Center/Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Yingying Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China.
| | - Hongxing Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China.
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Hwang J, Cho SM, Geocadin R, Ritzl EK. Methods of Evaluating EEG Reactivity in Adult Intensive Care Units: A Review. J Clin Neurophysiol 2024:00004691-990000000-00133. [PMID: 38857365 DOI: 10.1097/wnp.0000000000001078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
PURPOSE EEG reactivity (EEG-R) has become widely used in intensive care units for diagnosing and prognosticating patients with disorders of consciousness. Despite efforts toward standardization, including the establishment of terminology for critical care EEG in 2012, the processes of testing and interpreting EEG-R remain inconsistent. METHODS A review was conducted on PubMed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria consisted of articles published between January 2012, and November 2022, testing EEG-R on adult intensive care unit patients. Exclusion criteria included articles focused on highly specialized stimulation equipment or animal, basic science, or small case report studies. The Quality In Prognostic Studies tool was used to assess risk of bias. RESULTS One hundred and five articles were identified, with 26 variables collected for each. EEG-R testing varied greatly, including the number of stimuli (range: 1-8; 26 total described), stimulus length (range: 2-30 seconds), length between stimuli (range: 10 seconds-5 minutes), frequency of stimulus application (range: 1-9), frequency of EEG-R testing (range: 1-3 times daily), EEG electrodes (range: 4-64), personnel testing EEG-R (range: neurophysiologists to nonexperts), and sedation protocols (range: discontinuing all sedation to no attempt). EEG-R interpretation widely varied, including EEG-R definitions and grading scales, personnel interpreting EEG-R (range: EEG specialists to nonneurologists), use of quantitative methods, EEG filters, and time to detect EEG-R poststimulation (range: 1-30 seconds). CONCLUSIONS This study demonstrates the persistent heterogeneity of testing and interpreting EEG-R over the past decade, and contributing components were identified. Further many institutional efforts must be made toward standardization, focusing on the reproducibility and unification of these methods, and detailed documentation in the published literature.
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Affiliation(s)
- Jaeho Hwang
- Division of Epilepsy, Department of Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A
| | - Sung-Min Cho
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
| | - Romergryko Geocadin
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
| | - Eva K Ritzl
- Division of Epilepsy, Department of Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
- Division of Intraoperative Monitoring, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A
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Mathur R, Meyfroidt G, Robba C, Stevens RD. Neuromonitoring in the ICU - what, how and why? Curr Opin Crit Care 2024; 30:99-105. [PMID: 38441121 DOI: 10.1097/mcc.0000000000001138] [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: 03/12/2024]
Abstract
PURPOSE OF REVIEW We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury. RECENT FINDINGS Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms. SUMMARY The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.
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Affiliation(s)
- Rohan Mathur
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Geert Meyfroidt
- Department of Intensive Care Medicine, University Hospitals Leuven, Belgium and Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Belgium
| | - Chiara Robba
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Scienze Chirurgiche e Diagnostiche Integrate, Università degli Studi di Genova, Genova, Italy
| | - Robert D Stevens
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA
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Qing K, Forgacs P, Schiff N. EEG Pattern With Spectral Analysis Can Prognosticate Good and Poor Neurologic Outcomes After Cardiac Arrest. J Clin Neurophysiol 2024; 41:236-244. [PMID: 36007069 PMCID: PMC9905375 DOI: 10.1097/wnp.0000000000000958] [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] [Indexed: 11/26/2022] Open
Abstract
PURPOSE To investigate the prognostic value of a simple stratification system of electroencephalographical (EEG) patterns and spectral types for patients after cardiac arrest. METHODS In this prospectively enrolled cohort, using manually selected EEG segments, patients after cardiac arrest were stratified into five independent EEG patterns (based on background continuity and burden of highly epileptiform discharges) and four independent power spectral types (based on the presence of frequency components). The primary outcome is cerebral performance category (CPC) at discharge. Results from multimodal prognostication testing were included for comparison. RESULTS Of a total of 72 patients, 6 had CPC 1-2 by discharge, all of whom had mostly continuous EEG background without highly epileptiform activity at day 3. However, for the same EEG background pattern at day 3, 19 patients were discharged at CPC 3 and 15 patients at CPC 4-5. After adding spectral analysis, overall sensitivity for predicting good outcomes (CPC 1-2) was 83.3% (95% confidence interval 35.9% to 99.6%) and specificity was 97.0% (89.5% to 99.6%). In this cohort, standard prognostication testing all yielded 100% specificity but low sensitivity, with imaging being the most sensitive at 54.1% (36.9% to 70.5%). CONCLUSIONS Adding spectral analysis to qualitative EEG analysis may further improve the diagnostic accuracy of EEG and may aid developing novel measures linked to good outcomes in postcardiac arrest coma.
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Affiliation(s)
- Kurt Qing
- New York-Presbyterian Weill Cornell Medical Center
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Chen CC, Massey SL, Kirschen MP, Yuan I, Padiyath A, Simpao AF, Tsui FR. Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review. Resuscitation 2024; 194:110049. [PMID: 37972682 PMCID: PMC11023717 DOI: 10.1016/j.resuscitation.2023.110049] [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: 09/18/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
AIM OF THE REVIEW The primary aim of this systematic review was to investigate the most common electroencephalogram (EEG)-based machine learning (ML) model with the highest Area Under Receiver Operating Characteristic Curve (AUC) in two ML categories, conventional ML and Deep Neural Network (DNN), to predict the neurologic outcomes after cardiac arrest; the secondary aim was to investigate common EEG features applied to ML models. METHODS Systematic search of medical literature from PubMed and engineering literature from Compendex up to June 2, 2023. One reviewer screened studies that used EEG-based ML models to predict the neurologic outcomes after cardiac arrest. Four reviewers validated that the studies met selection criteria. Nine variables were manually extracted. The top-five common EEG features were calculated. We evaluated each study's risk of bias using the Quality in Prognosis Studies guideline. RESULTS Out of 351 identified studies, 17 studies met the inclusion criteria. Random Forest (RF) (n = 7) was the most common ML model in the conventional ML category (n = 11), followed by Convolutional Neural Network (CNN) (n = 4) in the DNN category (n = 6). The AUCs for RF ranged between 0.8 and 0.97, while CNN had AUCs between 0.7 and 0.92. The top-three commonly used EEG features were band power (n = 12), Shannon's Entropy (n = 11), burst-suppression ratio (n = 9). CONCLUSIONS RF and CNN were the two most common ML models with the highest AUCs for predicting the neurologic outcomes after cardiac arrest. Using a multimodal model that combines EEG features and electronic health record data may further improve prognostic performance.
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Affiliation(s)
- Chao-Chen Chen
- Tsui Laboratory, Children's Hospital of Philadelphia, 734 Schuylkill Ave, Philadelphia, PA 19146, United States; Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210 S 33rd St, Philadelphia, PA 19104, United States
| | - Shavonne L Massey
- Department of Neurology and Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Matthew P Kirschen
- Department of Neurology and Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Ian Yuan
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Asif Padiyath
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Allan F Simpao
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Fuchiang Rich Tsui
- Tsui Laboratory, Children's Hospital of Philadelphia, 734 Schuylkill Ave, Philadelphia, PA 19146, United States; Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States.
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Huang XF, Xu MX, Chen YF, Lin YQ, Lin YX, Wang F. Serum neuronal pentraxin 2 is related to cognitive dysfunction and electroencephalogram slow wave/fast wave frequency ratio in epilepsy. World J Psychiatry 2023; 13:714-723. [PMID: 38058685 PMCID: PMC10696288 DOI: 10.5498/wjp.v13.i10.714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/08/2023] [Accepted: 09/22/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Cognitive dysfunction in epileptic patients is a high-incidence complication. Its mechanism is related to nervous system damage during seizures, but there is no effective diagnostic biomarker. Neuronal pentraxin 2 (NPTX2) is thought to play a vital role in neurotransmission and the maintenance of synaptic plasticity. This study explored how serum NPTX2 and electroencephalogram (EEG) slow wave/fast wave frequency ratio relate to cognitive dysfunction in patients with epilepsy. AIM To determine if serum NPTX2 could serve as a potential biomarker for diagnosing cognitive impairment in epilepsy patients. METHODS The participants of this study, conducted from January 2020 to December 2021, comprised 74 epilepsy patients with normal cognitive function (normal group), 37 epilepsy patients with cognitive dysfunction [epilepsy patients with cognitive dysfunction (ECD) group] and 30 healthy people (control group). The mini-mental state examination (MMSE) scale was used to evaluate cognitive function. We determined serum NPTX2 levels using an enzyme-linked immunosorbent kit and calculated the signal value of EEG regions according to the EEG recording. Pearson correlation coefficient was used to analyze the correlation between serum NPTX2 and the MMSE score. RESULTS The serum NPTX2 level in the control group, normal group and ECD group were 240.00 ± 35.06 pg/mL, 235.80 ± 38.01 pg/mL and 193.80 ± 42.72 pg/mL, respectively. The MMSE score was lowest in the ECD group among the three, while no significant difference was observed between the control and normal groups. In epilepsy patients with cognitive dysfunction, NPTX2 level had a positive correlation with the MMSE score (r = 0.367, P = 0.0253) and a negative correlation with epilepsy duration (r = -0.443, P = 0.0061) and the EEG slow wave/fast wave frequency ratio value in the temporal region (r = -0.339, P = 0.039). CONCLUSION Serum NPTX2 was found to be related to cognitive dysfunction and the EEG slow wave/fast wave frequency ratio in patients with epilepsy. It is thus a potential biomarker for the diagnosis of cognitive impairment in patients with epilepsy.
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Affiliation(s)
- Xiao-Fen Huang
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Ming-Xia Xu
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Yue-Fan Chen
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Yun-Qing Lin
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Yuan-Xiang Lin
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Feng Wang
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, Fujian Province, China
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Sun X, Zhao J, Guo C, Zhu X. Early Prediction of Epilepsy after Encephalitis in Childhood Based on EEG and Clinical Features. Emerg Med Int 2023; 2023:8862598. [PMID: 37485251 PMCID: PMC10359137 DOI: 10.1155/2023/8862598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/20/2023] [Accepted: 03/30/2023] [Indexed: 07/25/2023] Open
Abstract
Objective The present study was designed to establish and evaluate an early prediction model of epilepsy after encephalitis in childhood based on electroencephalogram (ECG) and clinical features. Methods 255 patients with encephalitis were randomly divided into training and verification sets and were divided into postencephalitic epilepsy (PE) and no postencephalitic epilepsy (no-PE) according to whether epilepsy occurred one year after discharge. Univariate and multivariate logistic regression analyses were used to screen the risk factors for PE. The identified risk factors were used to establish and verify a model. Results This study included 255 patients with encephalitis, including 209 in the non-PE group and 46 in the PE group. Univariate and multiple logistic regression analysis showed that hemoglobin (OR = 0.968, 95% CI = 0.951-0.958), epilepsy frequency (OR = 0.968, 95% CI = 0.951-0.958), and ECG slow wave/fast wave frequency (S/F) in the occipital region were independent influencing factors for PE (P < 0.05).The prediction model is based on the above factors: -0.031 × hemoglobin -2.113 × epilepsy frequency + 7.836 × occipital region S/F + 1.595. In the training set and the validation set, the area under the ROC curve (AUC) of the model for the diagnosis of PE was 0.835 and 0.712, respectively. Conclusion The peripheral blood hemoglobin, the number of epileptic seizures in the acute stage of encephalitis, and EEG slow wave/fast wave frequencies can be used as predictors of epilepsy after encephalitis.
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Affiliation(s)
- Xiaojuan Sun
- Department of Pediatrics, The Second Affiliated Hospital of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu, China
| | - Jinhua Zhao
- Department of Pediatrics, The Second Affiliated Hospital of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu, China
| | - Chunyun Guo
- Department of Pediatrics, The Second Affiliated Hospital of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu, China
| | - Xiaoxiao Zhu
- Department of Pediatrics, The Second Affiliated Hospital of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu, China
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Jiang S, Chen W, Ren Z, Zhu H. EEG-based analysis for pilots' at-risk cognitive competency identification using RF-CNN algorithm. Front Neurosci 2023; 17:1172103. [PMID: 37152589 PMCID: PMC10160375 DOI: 10.3389/fnins.2023.1172103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots' SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/β (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), θ/(α + θ) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (α + θ)/β (p = 0.046 < 0.05 in F and p = 0.012 < 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots' cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.
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Affiliation(s)
- Shaoqi Jiang
- College of Information Engineering, Jinhua Polytechnic, Jinhua, Zhejiang, China
- College of Environment and Engineering, Shanghai Maritime University, Shanghai, China
- *Correspondence: Shaoqi Jiang,
| | - Weijiong Chen
- College of Environment and Engineering, Shanghai Maritime University, Shanghai, China
- College of Merchant Marine, Shanghai Maritime University, Shanghai, China
| | - Zhenzhen Ren
- College of Merchant Marine, Shanghai Maritime University, Shanghai, China
| | - He Zhu
- College of Information Engineering, Jinhua Polytechnic, Jinhua, Zhejiang, China
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Benghanem S, Pruvost-Robieux E, Bouchereau E, Gavaret M, Cariou A. Prognostication after cardiac arrest: how EEG and evoked potentials may improve the challenge. Ann Intensive Care 2022; 12:111. [PMID: 36480063 PMCID: PMC9732180 DOI: 10.1186/s13613-022-01083-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/07/2022] [Indexed: 12/13/2022] Open
Abstract
About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50% of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is important to provide correct information to patient's relatives, avoid disproportionate care in patients with irreversible hypoxic-ischemic brain injury (HIBI) and inappropriate withdrawal of care in patients with a possible favorable neurological recovery. ERC/ESICM 2021 algorithm allows a classification as "poor outcome likely" in 32%, the outcome remaining "indeterminate" in 68%. The crucial question is to know how we could improve the assessment of both unfavorable but also favorable outcome prediction. Neurophysiological tests, i.e., electroencephalography (EEG) and evoked-potentials (EPs) are a non-invasive bedside investigations. The EEG is the record of brain electrical fields, characterized by a high temporal resolution but a low spatial resolution. EEG is largely available, and represented the most widely tool use in recent survey examining current neuro-prognostication practices. The severity of HIBI is correlated with the predominant frequency and background continuity of EEG leading to "highly malignant" patterns as suppression or burst suppression in the most severe HIBI. EPs differ from EEG signals as they are stimulus induced and represent the summated activities of large populations of neurons firing in synchrony, requiring the average of numerous stimulations. Different EPs (i.e., somato sensory EPs (SSEPs), brainstem auditory EPs (BAEPs), middle latency auditory EPs (MLAEPs) and long latency event-related potentials (ERPs) with mismatch negativity (MMN) and P300 responses) can be assessed in ICU, with different brain generators and prognostic values. In the present review, we summarize EEG and EPs signal generators, recording modalities, interpretation and prognostic values of these different neurophysiological tools. Finally, we assess the perspective for futures neurophysiological investigations, aiming to reduce prognostic uncertainty in comatose and disorders of consciousness (DoC) patients after CA.
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Affiliation(s)
- Sarah Benghanem
- grid.411784.f0000 0001 0274 3893Medical ICU, Cochin Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France ,grid.508487.60000 0004 7885 7602Medical School, University Paris Cité, Paris, France ,After ROSC Network, Paris, France ,grid.7429.80000000121866389UMR 1266, Institut de Psychiatrie et, INSERM FHU NeuroVascNeurosciences de Paris-IPNP, 75014 Paris, France
| | - Estelle Pruvost-Robieux
- grid.508487.60000 0004 7885 7602Medical School, University Paris Cité, Paris, France ,Neurophysiology and Epileptology Department, GHU Psychiatry and Neurosciences, Sainte Anne, 75014 Paris, France ,grid.7429.80000000121866389UMR 1266, Institut de Psychiatrie et, INSERM FHU NeuroVascNeurosciences de Paris-IPNP, 75014 Paris, France
| | - Eléonore Bouchereau
- Department of Neurocritical Care, G.H.U Paris Psychiatry and Neurosciences, 1, Rue Cabanis, 75014 Paris, France ,grid.7429.80000000121866389UMR 1266, Institut de Psychiatrie et, INSERM FHU NeuroVascNeurosciences de Paris-IPNP, 75014 Paris, France
| | - Martine Gavaret
- grid.508487.60000 0004 7885 7602Medical School, University Paris Cité, Paris, France ,Neurophysiology and Epileptology Department, GHU Psychiatry and Neurosciences, Sainte Anne, 75014 Paris, France ,grid.7429.80000000121866389UMR 1266, Institut de Psychiatrie et, INSERM FHU NeuroVascNeurosciences de Paris-IPNP, 75014 Paris, France
| | - Alain Cariou
- grid.411784.f0000 0001 0274 3893Medical ICU, Cochin Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France ,grid.508487.60000 0004 7885 7602Medical School, University Paris Cité, Paris, France ,After ROSC Network, Paris, France ,grid.462416.30000 0004 0495 1460Paris-Cardiovascular-Research-Center (Sudden-Death-Expertise-Center), INSERM U970, Paris, France
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Hwang J, Cho SM, Ritzl EK. Recent applications of quantitative electroencephalography in adult intensive care units: a comprehensive review. J Neurol 2022; 269:6290-6309. [DOI: 10.1007/s00415-022-11337-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 10/15/2022]
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Johnsen B, Jeppesen J, Duez CHV. Common patterns of EEG reactivity in post-anoxic coma identified by quantitative analyses. Clin Neurophysiol 2022; 142:143-153. [PMID: 36041343 DOI: 10.1016/j.clinph.2022.07.507] [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: 01/11/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method. METHODS Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12-24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores. RESULTS EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5-8.5 seconds after stimulation and a decrease in theta activity 0.5-4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771-0.932). CONCLUSIONS Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication. SIGNIFICANCE This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.
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Affiliation(s)
- Birger Johnsen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark.
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark
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Benghanem S, Nguyen LS, Gavaret M, Mira JP, Pène F, Charpentier J, Marchi A, Cariou A. SSEP N20 and P25 amplitudes predict poor and good neurologic outcomes after cardiac arrest. Ann Intensive Care 2022; 12:25. [PMID: 35290522 PMCID: PMC8924339 DOI: 10.1186/s13613-022-00999-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/27/2022] [Indexed: 11/18/2022] Open
Abstract
Background To assess in comatose patients after cardiac arrest (CA) if amplitudes of two somatosensory evoked potentials (SSEP) responses, namely, N20-baseline (N20-b) and N20–P25, are predictive of neurological outcome. Methods Monocentric prospective study in a tertiary cardiac center between Nov 2019 and July-2021. All patients comatose at 72 h after CA with at least one SSEP recorded were included. The N20-b and N20–P25 amplitudes were automatically measured in microvolts (µV), along with other recommended prognostic markers (status myoclonus, neuron-specific enolase levels at 2 and 3 days, and EEG pattern). We assessed the predictive value of SSEP for neurologic outcome using the best Cerebral Performance Categories (CPC1 or 2 as good outcome) at 3 months (main endpoint) and 6 months (secondary endpoint). Specificity and sensitivity of different thresholds of SSEP amplitudes, alone or in combination with other prognostic markers, were calculated. Results Among 82 patients, a poor outcome (CPC 3–5) was observed in 78% of patients at 3 months. The median time to SSEP recording was 3(2–4) days after CA, with a pattern “bilaterally absent” in 19 patients, “unilaterally present” in 4, and “bilaterally present” in 59 patients. The median N20-b amplitudes were different between patients with poor and good outcomes, i.e., 0.93 [0–2.05]µV vs. 1.56 [1.24–2.75]µV, respectively (p < 0.0001), as the median N20–P25 amplitudes (0.57 [0–1.43]µV in poor outcome vs. 2.64 [1.39–3.80]µV in good outcome patients p < 0.0001). An N20-b > 2 µV predicted good outcome with a specificity of 73% and a moderate sensitivity of 39%, although an N20–P25 > 3.2 µV was 93% specific and only 30% sensitive. A low voltage N20-b < 0.88 µV and N20–P25 < 1 µV predicted poor outcome with a high specificity (sp = 94% and 93%, respectively) and a moderate sensitivity (se = 50% and 66%). Association of “bilaterally absent or low voltage SSEP” patterns increased the sensitivity significantly as compared to “bilaterally absent” SSEP alone (se = 58 vs. 30%, p = 0.002) for prediction of poor outcome. Conclusion In comatose patient after CA, both N20-b and N20–P25 amplitudes could predict both good and poor outcomes with high specificity but low to moderate sensitivity. Our results suggest that caution is needed regarding SSEP amplitudes in clinical routine, and that these indicators should be used in a multimodal approach for prognostication after cardiac arrest. Supplementary Information The online version contains supplementary material available at 10.1186/s13613-022-00999-6.
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Affiliation(s)
- Sarah Benghanem
- Medical ICU, Cochin Hospital, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014, Paris, France. .,Medical School, University of Paris, Paris, France. .,After ROSC Network, Paris, France. .,INSERM 1266, Institut de Psychiatrie et Neurosciences de Paris-IPNP, Sainte Anne Hospital, Paris, France.
| | - Lee S Nguyen
- CMC Ambroise Paré, Research and Innovation, Neuilly-sur-Seine, France
| | - Martine Gavaret
- Medical School, University of Paris, Paris, France.,Neurophysiology Department, GHU Psychiatrie et Neurosciences, Sainte Anne Hospital, Paris, France.,INSERM 1266, Institut de Psychiatrie et Neurosciences de Paris-IPNP, Sainte Anne Hospital, Paris, France
| | - Jean-Paul Mira
- Medical ICU, Cochin Hospital, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014, Paris, France.,Medical School, University of Paris, Paris, France
| | - Frédéric Pène
- Medical ICU, Cochin Hospital, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014, Paris, France.,Medical School, University of Paris, Paris, France
| | - Julien Charpentier
- Medical ICU, Cochin Hospital, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014, Paris, France
| | - Angela Marchi
- Medical School, University of Paris, Paris, France.,Neurophysiology Department, GHU Psychiatrie et Neurosciences, Sainte Anne Hospital, Paris, France.,INSERM 1266, Institut de Psychiatrie et Neurosciences de Paris-IPNP, Sainte Anne Hospital, Paris, France
| | - Alain Cariou
- Medical ICU, Cochin Hospital, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014, Paris, France.,Medical School, University of Paris, Paris, France.,After ROSC Network, Paris, France.,Paris-Cardiovascular-Research-Center (Sudden-Death-Expertise-Center), INSERM U970, Paris, France
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