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Sato K, Hitomi T, Kobayashi K, Matsuhashi M, Shimotake A, Kuzuya A, Kinoshita A, Matsumoto R, Takechi H, Sugi T, Nishida S, Takahashi R, Ikeda A. Electroencephalography can Ubiquitously Delineate the Brain Dysfunction of Neurodegenerative Dementia by Both Visual and Automatic Analysis Methods: A Preliminary Study. Clin EEG Neurosci 2025; 56:185-196. [PMID: 39363628 DOI: 10.1177/15500594241283512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/05/2024]
Abstract
Introduction: The aim was to examine the differences in electroencephalography (EEG) findings by visual and automated quantitative analyses between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Methods: EEG data of 20 patients with AD and 24 with DLB/PDD (12 DLB and 12 PDD) were retrospectively analyzed. Based on the awake EEG, the posterior dominant rhythm frequency and proportion of patients who showed intermittent focal and diffuse slow waves (IDS) were visually and automatically compared between the AD and DLB/PDD groups. Results: On visual analysis, patients with DLB/PDD showed a lower PDR frequency than patients with AD. In patients with PDR <8 Hz and occipital slow waves or patients with PDR <8 Hz and IDS, DLB/PDD was highly suspected (PPV 100%) and AD was unlikely (PPV 0%). On automatic analysis, the findings of the PDR were similar to those on visual analysis. Comparisons between visual and automatic analysis showed an overlap in the focal slow wave commonly detected by both methods in 10 of 44 patients, and concordant presence or absence of IDS in 29 of 43 patients. With respect to PDR <8 Hz and the combination of PDR <8 Hz and IDS, PPV and NPV in DLB/PDD and AD were not different between visual and automatic analysis. Conclusions: As the noninvasive, widely available clinical tool of low expense, visual analysis of EEG findings provided highly sufficient information to delineate different brain dysfunction in AD and DLB/PDD, and automatic EEG analysis could support visual analysis especially about PD.
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Affiliation(s)
- Kei Sato
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Takefumi Hitomi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Clinical Laboratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Katsuya Kobayashi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akihiro Shimotake
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akira Kuzuya
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ayae Kinoshita
- School of Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Riki Matsumoto
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Division of Neurology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Hajime Takechi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Geriatrics and Cognitive Disorders, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Takenao Sugi
- Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, Saga University, Saga, Japan
| | - Shigeto Nishida
- Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Olejarczyk E, Sobieszek A, Assenza G. Automatic Detection of the EEG Spike-Wave Patterns in Epilepsy: Evaluation of the Effects of Transcranial Current Stimulation Therapy. Int J Mol Sci 2024; 25:9122. [PMID: 39201808 PMCID: PMC11354554 DOI: 10.3390/ijms25169122] [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: 06/25/2024] [Revised: 08/13/2024] [Accepted: 08/16/2024] [Indexed: 09/03/2024] Open
Abstract
This study aims to develop a detection method based on morphological features of spike-wave (SW) patterns in the EEG of epilepsy patients and evaluate the effect of cathodal transcranial direct current stimulation (ctDCS) treatment. The proposed method is based on several simple features describing the shape of SW patterns and their synchronous occurrence on at least two EEG channels. High sensitivity, specificity and selectivity values were achieved for each patient and condition. ctDCS resulted in a significant reduction in the number of detected patterns, a decrease in spike duration and amplitude, and an increased spike mobility. The proposed method allows efficient identification of SW patterns regardless of brain condition, although the recruitment of patterns may be modified by ctDCS. This method can be useful in the clinical evaluation of ctDCS effects.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, 02-109 Warsaw, Poland
| | | | - Giovanni Assenza
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
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Sinkin M, Kvaskova N, Nogovitsyn V, Troitsky A, Ivanova I, Belyakova-Bodina A, Broutian A. [Translation and Adaptation for the Russian Language of the Revised Glossary of the Terms Most Commonly Used by Clinical Electroencephalographers and the Updated Proposal of the EEG Report Format (IFCN Revision 2017)]. Clin Neurophysiol Pract 2024; 9:138-161. [PMID: 38623401 PMCID: PMC11016809 DOI: 10.1016/j.cnp.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 12/10/2023] [Accepted: 01/02/2024] [Indexed: 04/17/2024] Open
Affiliation(s)
- Mikhail Sinkin
- N.V. Sklifosovsky Research Institute for Emergency Medicine, Moscow, Russia
| | | | | | - Alexey Troitsky
- Kazaryan Clinic of Epileptology and Neurology, Moscow, Russia
| | - Irina Ivanova
- Kazaryan Clinic of Epileptology and Neurology, Moscow, Russia
| | - Alexandra Belyakova-Bodina
- Kazaryan Clinic of Epileptology and Neurology, Moscow, Russia
- Research Center of Neurology, Moscow, Russia
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Strigaro G, Bisulli F, Assenza G, Mecarelli O, Grippo A, Meletti S, Alvisi L, Cantalupo G, Eleopra R, Guerra A, Lori S, Marinelli L, Tassi L, Tinuper P, Vigevano F. Traduzione e adattamento alla lingua italiana del glossario dei termini più comunemente usati dagli elettroencefalografisti clinici e proposta per il formato del referto EEG (Revisione IFCN 2017). Clin Neurophysiol Pract 2022; 7:325-365. [DOI: 10.1016/j.cnp.2022.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/04/2022] [Accepted: 09/25/2022] [Indexed: 11/10/2022] Open
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Paula Gonçalves A, Eduardo Silvado C, D'Andrea Meira I, Augusto Bragatti J, Otávio Caboclo L, Bittar Guaranha M, Oliveira da Conceição P, Alessandro Leite de Oliveira P, Ferrari Marinho T. Tradução e Adaptação para a Língua Portuguesa – Brasil do Glossário Revisado dos Termos Mais Comumente Usados por Eletroencefalografistas Clínicos e Proposta Atualizada do Formato do Laudo de EEG (IFCN Revisão 2017). Clin Neurophysiol Pract 2022; 7:78-95. [PMID: 35313603 PMCID: PMC8933678 DOI: 10.1016/j.cnp.2021.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 12/23/2021] [Indexed: 11/20/2022] Open
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Brogger J, Eichele T, Aanestad E, Olberg H, Hjelland I, Aurlien H. Visual EEG reviewing times with SCORE EEG. Clin Neurophysiol Pract 2018; 3:59-64. [PMID: 30215010 PMCID: PMC6133912 DOI: 10.1016/j.cnp.2018.03.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 02/27/2018] [Accepted: 03/05/2018] [Indexed: 11/15/2022] Open
Abstract
There is concern that the SCORE reporting standard for EEG takes too long. This study shows that a normal EEG can typically be reported in SCORE EEG in 8 min. Reviewing time is higher for abnormal recordings, and declined by 25% in this study.
Objective Visual EEG analysis is the gold standard for clinical EEG interpretation and analysis, but there is no published data on how long it takes to review and report an EEG in clinical routine. Estimates of reporting times may inform workforce planning and automation initiatives for EEG. The SCORE standard has recently been adopted to standardize clinical EEG reporting, but concern has been expressed about the time spent reporting. Methods Elapsed times were extracted from 5889 standard and sleep-deprived EEGs reported between 2015 and 2017 reported using the SCORE EEG software. Results The median review time for standard EEG was 12.5 min, and for sleep deprived EEG 20.9 min. A normal standard EEG had a median review time of 8.3 min. Abnormal EEGs took longer than normal EEGs to review, and had more variable review times. 99% of EEGs were reported within 24 h of end of recording. Review times declined by 25% during the study period. Conclusion Standard and sleep-deprived EEG review and reporting times with SCORE EEG are reasonable, increasing with increasing EEG complexity and decreasing with experience. EEG reports can be provided within 24 h. Significance Clinical standard and sleep-deprived EEG reporting with SCORE EEG has acceptable reporting times.
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Affiliation(s)
- Jan Brogger
- Section for Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Tom Eichele
- Section for Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, 5021 Bergen, Norway.,Department of Biological and Medical Psychology, University of Bergen, 5009 Bergen, Norway
| | - Eivind Aanestad
- Section for Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Henning Olberg
- Section for Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Ina Hjelland
- Section for Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Harald Aurlien
- Section for Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, 5021 Bergen, Norway
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Kane N, Acharya J, Beniczky S, Caboclo L, Finnigan S, Kaplan PW, Shibasaki H, Pressler R, van Putten MJ. A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017. Clin Neurophysiol Pract 2017; 2:170-185. [PMID: 30214992 PMCID: PMC6123891 DOI: 10.1016/j.cnp.2017.07.002] [Citation(s) in RCA: 298] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 07/14/2017] [Accepted: 07/14/2017] [Indexed: 11/06/2022] Open
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Qian D, Wang B, Qing X, Zhang T, Zhang Y, Wang X, Nakamura M. Drowsiness Detection by Bayesian-Copula Discriminant Classifier Based on EEG Signals During Daytime Short Nap. IEEE Trans Biomed Eng 2017; 64:743-754. [DOI: 10.1109/tbme.2016.2574812] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Consensus statement on continuous EEG in critically ill adults and children, part II: personnel, technical specifications, and clinical practice. J Clin Neurophysiol 2016; 32:96-108. [PMID: 25626777 DOI: 10.1097/wnp.0000000000000165] [Citation(s) in RCA: 170] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION Critical Care Continuous EEG (CCEEG) is a common procedure to monitor brain function in patients with altered mental status in intensive care units. There is significant variability in patient populations undergoing CCEEG and in technical specifications for CCEEG performance. METHODS The Critical Care Continuous EEG Task Force of the American Clinical Neurophysiology Society developed expert consensus recommendations on the use of CCEEG in critically ill adults and children. RECOMMENDATIONS The consensus panel describes the qualifications and responsibilities of CCEEG personnel including neurodiagnostic technologists and interpreting physicians. The panel outlines required equipment for CCEEG, including electrodes, EEG machine and amplifier specifications, equipment for polygraphic data acquisition, EEG and video review machines, central monitoring equipment, and network, remote access, and data storage equipment. The consensus panel also describes how CCEEG should be acquired, reviewed and interpreted. The panel suggests methods for patient selection and triage; initiation of CCEEG; daily maintenance of CCEEG; electrode removal and infection control; quantitative EEG techniques; EEG and behavioral monitoring by non-physician personnel; review, interpretation, and reports; and data storage protocols. CONCLUSION Recommended qualifications for CCEEG personnel and CCEEG technical specifications will facilitate standardization of this emerging technology.
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Qian D, Wang B, Qing Y, Zhang T, Zhang Y, Wang X, Nakamura M. Bayesian Nonnegative CP Decomposition-based Feature Extraction Algorithm for Drowsiness Detection. IEEE Trans Neural Syst Rehabil Eng 2016; 25:1297-1308. [DOI: 10.1109/tnsre.2016.2618902] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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van Diessen E, Numan T, van Dellen E, van der Kooi AW, Boersma M, Hofman D, van Lutterveld R, van Dijk BW, van Straaten ECW, Hillebrand A, Stam CJ. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin Neurophysiol 2015; 126:1468-81. [PMID: 25511636 DOI: 10.1016/j.clinph.2014.11.018] [Citation(s) in RCA: 259] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Revised: 10/30/2014] [Accepted: 11/20/2014] [Indexed: 12/17/2022]
Affiliation(s)
- E van Diessen
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands.
| | - T Numan
- Department of Intensive Care, University Medical Center Utrecht, The Netherlands
| | - E van Dellen
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands; Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A W van der Kooi
- Department of Intensive Care, University Medical Center Utrecht, The Netherlands
| | - M Boersma
- Department of Experimental Psychology, Utrecht University, The Netherlands
| | - D Hofman
- Department of Experimental Psychology, Utrecht University, The Netherlands
| | - R van Lutterveld
- Center for Mindfulness, University of Massachusetts School of Medicine, Worcester, Massachusetts, USA
| | - B W van Dijk
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - E C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
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Kayser J, Tenke CE. Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: A tutorial review. Int J Psychophysiol 2015; 97:189-209. [PMID: 25920962 DOI: 10.1016/j.ijpsycho.2015.04.012] [Citation(s) in RCA: 152] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 03/26/2015] [Accepted: 04/13/2015] [Indexed: 12/01/2022]
Abstract
Despite the recognition that the surface Laplacian may counteract adverse effects of volume conduction and recording reference for surface potential data, electrophysiology as a discipline has been reluctant to embrace this approach for data analysis. The reasons for such hesitation are manifold but often involve unfamiliarity with the nature of the underlying transformation, as well as intimidation by a perceived mathematical complexity, and concerns of signal loss, dense electrode array requirements, or susceptibility to noise. We revisit the pitfalls arising from volume conduction and the mandated arbitrary choice of EEG reference, describe the basic principle of the surface Laplacian transform in an intuitive fashion, and exemplify the differences between common reference schemes (nose, linked mastoids, average) and the surface Laplacian for frequently-measured EEG spectra (theta, alpha) and standard event-related potential (ERP) components, such as N1 or P3. We specifically review common reservations against the universal use of the surface Laplacian, which can be effectively addressed by employing spherical spline interpolations with an appropriate selection of the spline flexibility parameter and regularization constant. We argue from a pragmatic perspective that not only are these reservations unfounded but that the continued predominant use of surface potentials poses a considerable impediment on the progress of EEG and ERP research.
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Affiliation(s)
- Jürgen Kayser
- Division of Cognitive Neuroscience, New York State Psychiatric Institute, New York, NY, USA; Department of Psychiatry, Columbia University College of Physicians & Surgeons, New York, NY, USA.
| | - Craig E Tenke
- Division of Cognitive Neuroscience, New York State Psychiatric Institute, New York, NY, USA; Department of Psychiatry, Columbia University College of Physicians & Surgeons, New York, NY, USA
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