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Journal articles on the topic 'Clinical EEG data'

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Author: Grafiati

Published: 1 June 2024

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1

Járdánházy, T., I. Somogyi, and T. Asztalos. "Compression methods for EEG spectral data." Electroencephalography and Clinical Neurophysiology 87, no. 2 (August 1993): S133. http://dx.doi.org/10.1016/0013-4694(93)91489-n.

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2

Banquet, J. P., W. Guenther, and D. Breitling. "Multidimensional factorial methods for EEG data." Electroencephalography and Clinical Neurophysiology 61, no. 3 (September 1985): S231. http://dx.doi.org/10.1016/0013-4694(85)90874-0.

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Antony, Mary Judith, Baghavathi Priya Sankaralingam, Shakir Khan, Abrar Almjally, Nouf Abdullah Almujally, and Rakesh Kumar Mahendran. "Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data." Diagnostics 13, no. 17 (September 3, 2023): 2852. http://dx.doi.org/10.3390/diagnostics13172852.

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An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL–SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method’s ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.

4

Gu, Yuqiao, Geir Halnes, Hans Liljenström, and Björn Wahlund. "A cortical network model for clinical EEG data analysis." Neurocomputing 58-60 (June 2004): 1187–96. http://dx.doi.org/10.1016/j.neucom.2004.01.184.

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5

Goldenholz, Daniel M., Joseph J. Tharayil, Rubin Kuzniecky, Philippa Karoly, William H. Theodore, and Mark J. Cook. "Simulating clinical trials with and without intracranial EEG data." Epilepsia Open 2, no. 2 (January 18, 2017): 156–61. http://dx.doi.org/10.1002/epi4.12038.

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Ivanov, А. А. "Overview of mathematical EEG analysis. Quantitative EEG." Epilepsy and paroxysmal conditions 15, no. 2 (July 9, 2023): 171–92. http://dx.doi.org/10.17749/2077-8333/epi.par.con.2023.154.

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The purpose of this article is to familiarize medical specialists involved in registration and analysis of electroencephalographic (EEG) studies using methods of mathematical processing and analysis for recorded EEG data. Understanding the principles of how quantitative EEG analysis tools work should help medical personnel to properly use their capabilities and ultimately improve quality of medical care. Here, we discuss basic and innovative mathematical tools for EEG processing and analysis.

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Salam, Abdus, Selina Husna Banu, Abu Nayeem, and Zobaida Sultana Susan. "Clinical Finding of Electroencephalographic (EEG) Data in Adults: A Retrospective study." Journal of Shaheed Suhrawardy Medical College 6, no. 1 (March 7, 2017): 14–17. http://dx.doi.org/10.3329/jssmc.v6i1.31486.

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Background: Electroencephalography (EEG) is the first and only real-time monitor of epileptic seizures, and is a powerful measure of cerebral function in the seriously ill.Objective: The purpose of this study was to see the common conditions for doing EEG.Methods: This study was performed retrospectively by collecting and reviewing the electro-clinical information of the adult patients to whom EEGs were done at the 'Central Hospital' laboratory. Clinical problems were categorized into seizures, fainting attacks, headache, giddiness, vertigo, stroke, suicidal tendency, sudden aggressiveness and head injury. Routine EEGs were performed for 30 minutes. EEG findings were categorized as normal for the age, localized or generalized epileptiform discharges, non-epileptogenic dysfunction.Result: In total 53 adult patients 34% population had fainting attacks, 28% had seizures, 10% had stroke and 28% had complaint related to behavior, suicidal tendency, headache and post head injury problems. EEG was normal in 60%.Conclusion: EEG is advised for diverse conditions. The proportion of epileptic patients is small, although this is the principal indication for doing routine EEG.J Shaheed Suhrawardy Med Coll, June 2014, Vol.6(1); 14-17

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Noachtar, Soheyl, Jan Remi, and Elisabeth Kaufmann. "EEG-Update." Klinische Neurophysiologie 53, no. 04 (November 29, 2022): 243–52. http://dx.doi.org/10.1055/a-1949-1691.

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Durch die rasante Entwicklung digitaler Computertechniken und neuer Analysemethoden hat sich ein neuer Ansatz zur Analyse der Hirnströme (quantitatives EEG) ergeben, die in verschiedenen klinischen Bereichen der Neurologie und Psychiatrie bereits Ergebnisse zeigen. Die neuen Möglichkeiten der Analyse des EEG durch Einsatz künstlicher Intelligenz (Deep Learning) und großer Datenmengen (Big Data) sowie telemedizinischer Datenübermittlung und Interaktion wird den Einsatz der Methode vermutlich in den nächsten Jahren erweitern.

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Cincotti, F., C. Babiloni, C. Miniussi, F. Carducci, D. Moretti, S. Salinari, R. Pascual-Marqui, P. M. Rossini, and F. Babiloni. "EEG Deblurring Techniques in a Clinical Context." Methods of Information in Medicine 43, no. 01 (2004): 114–17. http://dx.doi.org/10.1055/s-0038-1633846.

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Summary Objectives: EEG scalp potential distributions recorded in humans are affected by low spatial resolution and by the dependence on the electrical reference used. High resolution EEG technologies are available to drastically increase the spatial resolution of the raw EEG. Such technologies include the computation of surface Laplacian (SL) of the recorded potentials, as well as the use of realistic head models to estimate the cortical sources via linear inverse procedure (low resolution brain electromagnetic tomography, LORETA). However, these deblurring procedures are generally used in conjunction with EEG recordings with 64-128 scalp electrodes and with realistic head models obtained via sequential magnetic resonance images (MRIs) of the subjects. Such recording setup it is not often available in the clinical context, due to both the unavailability of these technologies and the scarce compliance of the patients with them. In this study we addressed the use of SL and LORETA deblurring techniques to analyze data from a standard 10-20 system (19 electrodes) in a group of Alzheimer disease (AD) patients. Methods: EEG data related to unilateral finger movements were gathered from 10 patients affected by AD. SL and LORETA techniques were applied for source estimation of EEG data. The use of MRIs for the construction of head models was avoided by using the quasi-realistic head model of the Brain Imaging Neurology Institute of Montreal. Results: A similar cortical activity estimated by the SL and LORETA techniques was observed during an identical time period of the acquired EEG data in the examined population. Conclusions: The results of the present study suggest that both SL and LORETA approaches can be usefully applied in the clinical context, by using quasi-realistic head modeling and a standard 10-20 system as electrode montage (19 electrodes). These results represent a reciprocal cross-validation of the two mathematically independent techniques in a clinical environment.

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Kutafina, Ekaterina, Alexander Brenner, Yannic Titgemeyer, Rainer Surges, and Stephan Jonas. "Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection." PeerJ 8 (May 1, 2020): e8969. http://dx.doi.org/10.7717/peerj.8969.

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Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we developed a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are recorded simultaneously during 30 min long sessions in resting state. The data are collected from 22 patients with epileptiform abnormalities in EEG. In order to compare two multichannel EEG signals with differently placed references and electrodes, a novel data processing pipeline is proposed. It allows deriving matching pairs of time series which are suitable for similarity assessment through Pearson correlation. The average correlation of 0.64 is achieved on a test dataset, which can be considered a promising result, taking the positions shift due to the simultaneous electrode placement into account.

11

da Silva Lourenço, Catarina, Marleen C. Tjepkema-Cloostermans, and Michel J. A. M. van Putten. "Efficient use of clinical EEG data for deep learning in epilepsy." Clinical Neurophysiology 132, no. 6 (June 2021): 1234–40. http://dx.doi.org/10.1016/j.clinph.2021.01.035.

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12

Natale, E., A. Mattaliano, G. Alia, M. G. Perpero, and O. Daniele. "‘Alpha pattern’ coma: Clinical and EEG data, aetiology, evolution and prognosis." Electroencephalography and Clinical Neurophysiology 75 (January 1990): S103. http://dx.doi.org/10.1016/0013-4694(90)92091-a.

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13

Gastaut, H., N. Pinsard, C. Raybaud, and B. Zifkin. "Clinical data, EEG patterns, and CT scanning in the lissencephaly syndrome." Electroencephalography and Clinical Neurophysiology 61, no. 3 (September 1985): S168. http://dx.doi.org/10.1016/0013-4694(85)90646-7.

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14

Swart, M. D., E. J. Jonkman, and A. W. de Weerd. "P451 Late changes after stroke: Clinical, EEG, and CT-scan data." Electroencephalography and Clinical Neurophysiology 99, no. 4 (October 1996): 379. http://dx.doi.org/10.1016/0013-4694(96)88626-3.

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15

Glaser, J., V. Schöpf, R. Beisteiner, H. Bauer, and F. Fischmeister. "Optimum gradient artifact removal from EEG-data using facet." Journal of the Neurological Sciences 333 (October 2013): e622. http://dx.doi.org/10.1016/j.jns.2013.07.2164.

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16

Krystal, Andrew D., Henry S. Greenside, Paul S. Rapp, Alfonso Albano, Chris Cellucci, and Richard D. Weiner. "PARTIAL LEAST SQUARES ANALYSIS OF MULTICHANNEL EEG DATA." Journal of Clinical Neurophysiology 15, no. 3 (May 1998): 274. http://dx.doi.org/10.1097/00004691-199805000-00028.

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17

Kemp, Bob, Teunis van Beelen, Marion Stijl, Paul van Someren, Marco Roessen, and J. Gert van Dijk. "A DC attenuator allows common EEG equipment to record fullband EEG, and fits fullband EEG into standard European Data Format." Clinical Neurophysiology 121, no. 12 (December 2010): 1992–97. http://dx.doi.org/10.1016/j.clinph.2010.05.006.

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18

Klotz, Jürgen Michael. "Topographic EEG Mapping Methods." Cephalalgia 13, no. 1 (February 1993): 45–52. http://dx.doi.org/10.1046/j.1468-2982.1993.1301045.x.

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After almost 40 years of research on EEG computer analysis, present clinical applications of this method remain limited. At the present time, EEG mapping is suited primarily for research. Despite the pitfalls of an uncritical application of EEG mapping, progress in clinical research made possible by EEG mapping techniques has been considerable. Some problems of data acquisition, display and statistical analysis are discussed in this paper. For headache research examination of the activated EEG, especially with photic stimulation, has greater diagnostic importance than mapping under resting conditions.

19

Kira, Jun-ichi, Sei-ichiro Minato, Yasuto Itoyama, Ikuo Goto, Motohiro Kato, and Kanehiro Hasuo. "Leukoencephalopathy in HTLV-I-associated myelopathy: MRI and EEG data." Journal of the Neurological Sciences 87, no. 2-3 (November 1988): 221–32. http://dx.doi.org/10.1016/0022-510x(88)90247-x.

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20

Hunter, M., R. L. L. Smith, W. Hyslop, O. A. Rosso, R. Gerlach, J. A. P. Rostas, D. B. Williams, and F. Henskens. "The Australian EEG Database." Clinical EEG and Neuroscience 36, no. 2 (April 2005): 76–81. http://dx.doi.org/10.1177/155005940503600206.

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The Australian EEG Database is a web-based de-identified searchable database of 18,500 EEG records recorded at a regional public hospital over an 11-year period. Patients range in age from a premature infant born at 24 weeks gestation, through to people aged over 90 years. This paper will describe the history of the database, the range of patients represented in the database, and the nature of the text-based and digital data contained in the database. Preliminary results of the first two studies undertaken using the database are presented. Plans for sharing data from the Australian EEG database with researchers are discussed. We anticipate that such data will be useful in not only helping to answer clinical questions but also in the field of mathematical modeling of the EEG.

21

Vogrin, Simon J., and Chris Plummer. "EEG Source Imaging—Clinical Considerations for EEG Acquisition and Signal Processing for Improved Temporo-Spatial Resolution." Journal of Clinical Neurophysiology 41, no. 1 (January 2024): 8–18. http://dx.doi.org/10.1097/wnp.0000000000001023.

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Summary: EEG source imaging (ESI) has gained traction in recent years as a useful clinical tool for the noninvasive surgical work-up of patients with drug-resistant focal epilepsy. Despite its proven benefits for the temporo-spatial modeling of spike and seizure sources, ESI remains widely underused in clinical practice. This partly relates to a lack of clarity around an optimal approach to the acquisition and processing of scalp EEG data for the purpose of ESI. Here, we describe some of the practical considerations for the clinical application of ESI. We focus on patient preparation, the impact of electrode number and distribution across the scalp, the benefit of averaging raw data for signal analysis, and the relevance of modeling different phases of the interictal discharge as it evolves from take-off to peak. We emphasize the importance of recording high signal-to-noise ratio data for reliable source analysis. We argue that the accuracy of modeling cortical sources can be improved using higher electrode counts that include an inferior temporal array, by averaging interictal waveforms rather than limiting ESI to single spike analysis, and by careful interrogation of earlier phase components of these waveforms. No amount of postacquisition signal processing or source modeling sophistication, however, can make up for suboptimally recorded scalp EEG data in a poorly prepared patient.

22

Matsuo, Fumisuke. "Rapid Scanning of EEG Data in Long-Term Monitoring." Journal of Clinical Neurophysiology 5, no. 4 (October 1988): 336. http://dx.doi.org/10.1097/00004691-198810000-00032.

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23

Sarrigiannis, Ptolemaios G., Yifan Zhao, Hua-Liang Wei, Stephen A. Billings, Jayne Fotheringham, and Marios Hadjivassiliou. "Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data." Clinical Neurophysiology 125, no. 1 (January 2014): 32–46. http://dx.doi.org/10.1016/j.clinph.2013.06.012.

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24

Drobný, M., B. Drobná Sániová, S. Učňová, G. Sobolová, R. Koyš, C. Machado, and Ya Machado. "3D EEG and Clinical Evidence of Brain Dying. Preliminary Report." General Reanimatology 19, no. 1 (February 23, 2023): 34–42. http://dx.doi.org/10.15360/1813-9779-2023-1-34-42.

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Determination of brain dying means reversible or irreversible injury to the brain, including the brainstem. Current guidelines rely on clinical examination including the proof of coma, absent brain stem reﬂexes, and apnoea test. Neurophysiological testing using electroencephalography and evoked potentials — somatosensory evoked potentials and brainstem auditory evoked potential could have been helpful in the ﬁnal diagnostic brain death conclusion, but the diagnostic accuracy of these methods in the last years has revealed controversies. Here, we present data on quantitative EEG signal evaluation (qEEG) by a 3-dimensional brain mapping (3D BM) as developing tool to clarify whether the transverse and anterior posterior coherences such as connectivity indices may demonstrate connection in transversal or anterior posterior dimensions with «wavelet transformation» and if the 3D BM visualization of the of representative EEG signals may improve informative value of EEG signals quantiﬁcation when evaluating the brain dying.The purpose of our work is to provide an update on the evidence and controversies on the use of EEG for determining brain dying and raise discussion on EEG applications to improve the transplantation program.Results. We analyzed the EEG records of 10 patients admitted for cardiopulmonary resuscitation (CPR) during September, 2017 — August, 2018. Data from one patient, ŽM, 33 years old, after haemorrhagic shock (August 2018) were analyzed in details. Quantitative EEG dynamics by images and clinical course of brain dying were monitored prior and after the amantadine sulfate intravenous administration for brain revival. Data demonstrated the ability of brain to survive; the cause of ﬁnal brain death was heart failure.Conclusion. Data conﬁrm the hope for survival of the brain in a coma and demonstrate brain capability to keep functionally optimal state as a potential for a good social adaptation.

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Loula, P., E. Rauhala, M. Erkinjuntti, E. Raty, K. Hirvonen, and V. Hakkinen. "Distributed clinical neurophysiology." Journal of Telemedicine and Telecare 3, no. 2 (June 1, 1997): 89–95. http://dx.doi.org/10.1258/1357633971930922.

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We have developed a consultation forum for clinical neurophysiology in Finland. The system connects local digital electroencephalography EEG recording and analysing networks using a high-speed asynchronous transfer mode ATM network. Clinicians can obtain a second opinion using interactive data and video consultations or using data-only consultations. In addition, the system can be used for off-line review of prerecorded data. During a one-month evaluation, 66 EEG recordings were made altogether in Satakunta Central Hospital and consultations were required on 12 occasions. Nine of them were data-only consultations and three were data and video consultations. A data consultation lasted 15-20 min and a data and video consultation 35-45 min. Clinically, there were numerous benefits for the hospitals. The system established a link to a centre of excellence for second opinions or continuing education. It also helped with on-duty arrangements and enabled the construction of national data banks.

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Kozinska, D., F. Carducci, and K. Nowinski. "Automatic alignment of EEG/MEG and MRI data sets." Clinical Neurophysiology 112, no. 8 (August 2001): 1553–61. http://dx.doi.org/10.1016/s1388-2457(01)00556-9.

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27

Fousek, J. "13. Processing and visualization of high resolution EEG data." Clinical Neurophysiology 125, no. 5 (May 2014): e29. http://dx.doi.org/10.1016/j.clinph.2013.12.051.

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28

Whittingstall, Kevin, Gerhard Stroink, and Bruce Dick. "Dipole localization accuracy using grand-average EEG data sets." Clinical Neurophysiology 115, no. 9 (September 2004): 2108–12. http://dx.doi.org/10.1016/j.clinph.2004.04.004.

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29

Nielsen, Jonas Munch, Ástrós Eir Kristinsdóttir, Ivan Chrilles Zibrandtsen, Paolo Masulli, Martin Ballegaard, Tobias Søren Andersen, and Troels Wesenberg Kjær. "Out-of-hospital multimodal seizure detection: a pilot study." BMJ Neurology Open 5, no. 2 (August 2023): e000442. http://dx.doi.org/10.1136/bmjno-2023-000442.

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BackgroundOut-of-hospital seizure detection aims to provide clinicians and patients with objective seizure documentation in efforts to improve the clinical management of epilepsy. In-patient studies have found that combining different modalities helps improve the seizure detection accuracy. In this study, the objective was to evaluate the viability of out-of-hospital seizure detection using wearable ECG, accelerometry and behind-the-ear electroencephalography (EEG). Furthermore, we examined the signal quality of out-of-hospital EEG recordings.MethodsSeventeen patients were monitored for up to 5 days. A support vector machine based seizure detection algorithm was applied using both in-patient seizures and out-of-hospital electrographic seizures in one patient. To assess the content of noise in the EEG signal, we compared the root-mean-square (RMS) of the recordings to a reference threshold derived from manually categorised segments of EEG recordings.ResultsIn total 1427 hours of continuous EEG was recorded. In one patient, we identified 15 electrographic focal impaired awareness seizures with a motor component. After training our algorithm on in-patient data, we found a sensitivity of 91% and a false alarm rate (FAR) of 18/24 hours for the detection of out-of-hospital seizures using a combination of EEG and ECG recordings. We estimated that 30.1% of the recorded EEG signal was physiological EEG, with an RMS value within the reference threshold.ConclusionWe found that detection of out-of-hospital focal impaired awareness seizures with a motor component is possible and that applying multiple modalities improves the diagnostic accuracy compared with unimodal EEG. However, significant challenges remain regarding a high FAR and that only 30.1% of the EEG data represented usable signal.

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Wieser, H. G., S. Hailemariam, M. Regard, and T. Landis. "Unilateral Limbic Epileptic Status Activity: Stereo EEG, Behavioral, and Cognitive Data." Epilepsia 26, no. 1 (February 1985): 19–29. http://dx.doi.org/10.1111/j.1528-1157.1985.tb05184.x.

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31

Chen, Hsin Yi, Jonathan Elmer, Sahar F. Zafar, Manohar Ghanta, Valdery Moura Junior, Eric S. Rosenthal, Emily J. Gilmore, et al. "Combining Transcranial Doppler and EEG Data to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage." Neurology 98, no. 5 (November 29, 2021): e459-e469. http://dx.doi.org/10.1212/wnl.0000000000013126.

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Background and ObjectivesDelayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction.MethodsWe retrospectively assessed patients with moderate to severe SAH (2011–2015; Fisher 3–4 or Hunt-Hess 4–5) who had both prospective TCD and EEG acquisition during hospitalization. Middle cerebral artery (MCA) peak systolic velocities (PSVs) and the presence or absence of epileptiform abnormalities (EAs), defined as seizures, epileptiform discharges, and rhythmic/periodic activity, were recorded daily. Logistic regressions were used to identify significant covariates of EAs and TCD to predict DCI. Group-based trajectory modeling (GBTM) was used to account for changes over time by identifying distinct group trajectories of MCA PSV and EAs associated with DCI risk.ResultsWe assessed 107 patients; DCI developed in 56 (51.9%). Univariate predictors of DCI are presence of high-MCA velocity (PSV ≥200 cm/s, sensitivity 27%, specificity 89%) and EAs (sensitivity 66%, specificity 62%) on or before day 3. Two univariate GBTM trajectories of EAs predicted DCI (sensitivity 64%, specificity 62.75%). Logistic regression and GBTM models using both TCD and EEG monitoring performed better. The best logistic regression and GBTM models used both TCD and EEG data, Hunt-Hess score at admission, and aneurysm treatment as predictors of DCI (logistic regression: sensitivity 90%, specificity 70%; GBTM: sensitivity 89%, specificity 67%).DiscussionEEG and TCD biomarkers combined provide the best prediction of DCI. The conjunction of clinical variables with the timing of EAs and high MCA velocities improved model performance. These results suggest that TCD and cEEG are promising complementary monitoring modalities for DCI prediction. Our model has potential to serve as a decision support tool in SAH management.Classification of EvidenceThis study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after SAH.

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Birvinskas, Darius, Vacius Jusas, Ignas Martisius, and Robertas Damasevicius. "Fast DCT algorithms for EEG data compression in embedded systems." Computer Science and Information Systems 12, no. 1 (2015): 49–62. http://dx.doi.org/10.2298/csis140101083b.

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Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required.

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Jiang, Zhen, and Wenshan Zhao. "Fusion Algorithm for Imbalanced EEG Data Processing in Seizure Detection." Seizure 91 (October 2021): 207–11. http://dx.doi.org/10.1016/j.seizure.2021.06.023.

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Hur, Yun Jung, Andrew J. Kim, and Douglas R. Nordli. "MRI supersedes ictal EEG when other presurgical data are concordant." Seizure 53 (December 2017): 18–22. http://dx.doi.org/10.1016/j.seizure.2017.10.013.

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Litovchenko, Tetyana, Olga Sukhonosova, Oleksii Sorochan, Vladlena Salnikova, and Maryna Gekova. "Comparison of clinical, electroencephalographic and tomographic data in children with epilepsy with controlled and uncontrolled seizures." Ukrains'kyi Visnyk Psykhonevrolohii 27, no. 3 (September 5, 2019): 72–75. http://dx.doi.org/10.36927/2079-0325-v27-is3-2019-13.

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The article presents the results of a examination of 124 children with epilepsy aged 1 month to 18 years to detect clinical electroencephalographic (EEG) and tomographic correlations in patients with controlled and uncontrolled seizures. It was shown that clinical manifestations (seizure types) are not always correlated with local changes on EEG and focus on MRI. In children, especially with uncontrolled seizures, even in the case of a focal onset of the seizure, secondary generalization is often observed, which is due to the functional immaturity of the brain and the tendency to rapid generalization of the epileptic potential. In general, the coincidence between the EEG data and morphological MRI is determined in 66 % of patients, when performing high-fi eld magnetic resonance tomography (on devices with a magnetic fi eld of 1.5 T or more) in the "Epilepsy" mode — in 71 %, and using MR-spectroscopy rises to 73 %. Key words: children, epilepsy, EEG, MRI

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Arns, M. "EEG and ECG based response predictors in depression: Time for personalised medicine or treatment stratification?" European Psychiatry 64, S1 (April 2021): S6—S7. http://dx.doi.org/10.1192/j.eurpsy.2021.40.

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In depression (MDD) treatment there is a clear need for novel treatments, biomarkers and individualized treatment approaches. One of the most promising and most widely investigated biomarkers for antidepressant treatments is the EEG. Most EEG biomarkers however, still lack robustness and reproducibility and suffer significant publication bias as highlighted in a recent meta-analysis (Widge et al., 2018). Therefore, large controlled validation studies are needed with a focus on robustness, replication and clinical relevance. In this presentation results will be presented from the largest EEG Biomarker study to date, the international Study to Predict Optimized Treatment in Depression (iSPOT-D), where 1008 MDD patients were randomized to Escitalopram, Sertraline and Venlafaxine. Drug-class specific (Arns et al., 2016) and drug-specific (Arns, Gordon & Boutros, 2015) biomarkers will be highlighted as well as preliminary data from a prospective feasibility trial. Furthermore, data will be presented on repetitive Transcranial Magnetic Stimulation (rTMS) treatment in MDD on EEG and clinical predictors (Krepel et al., 2018; 2019) and a new method called Neuro-Cardiac-Guided TMS (NCG TMS), that exploits network connectivity in the frontal vagal pathway, as a target engagement approach (Iseger et al., 2019). Finally, clinical implications and implementations will be discussed from a ‘treatment stratification’ perspective, which might be a more realistic goal relative to ‘personalized medicine’ perspective.DisclosureMA is unpaid research director of the Brainclinics Foundation, a minority shareholder in neuroCare Group (Munich, Germany), and a co-inventor on 4 patent applications related to EEG, neuromodulation and psychophysiology, but receives no royalties related

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Vanhatalo, Sampsa, Juha Voipio, and Kai Kaila. "Full-Band EEG (FbEEG): A New Standard for Clinical Electroencephalography." Clinical EEG and Neuroscience 36, no. 4 (October 2005): 311–17. http://dx.doi.org/10.1177/155005940503600411.

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A variety of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and magnetoencephalography (MEG), have been established during the last few decades, with progressive improvements continuously taking place in the underlying technologies. In contrast to this, the recording bandwidth of the routine clinical EEG (typically around 0.5–50 Hz) that was originally set by trivial technical limitations has remained practically unaltered for over half a decade. An increasing amount of evidence shows that salient EEG signals take place and can be recorded beyond the conventional clinical EEG bandwidth. These physiological and pathological EEG activities range from 0.01 Hz to several hundred Hz, and they have been demonstrated in recordings of spontaneous activity in the preterm human brain, and during epileptic seizures, sleep, as well as in various kinds of cognitive tasks and states in the adult brain. In the present paper, we will describe the practical aspects of recording the full physiological frequency band of the EEG (Full-band EEG; FbEEG), and we review the currently available data on the clinical applications of FbEEG. Recording the FbEEG is readily attained with commercially available direct-current (DC) coupled amplifiers if the recording setup includes electrodes providing a DC-stable electrode-skin interface. FbEEG does not have trade-offs that would favor any frequency band at the expense of another. We present several arguments showing that elimination of the lower ( infraslow) or higher ( ultrafast) bands of the EEG frequency spectrum in routine EEG has led, and will lead, to situations where salient and physiologically meaningful features of brain activity remain undetected or become seriously attenuated and distorted. With the currently available electrode, amplifier and data acquisition technology, it is to be expected that FbEEG will become the standard approach in both clinical and basic science.

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Andraus, Maria Emilia Cosenza, Cesar Fantezia Andraus, and Soniza Vieira Alves-Leon. "Periodic EEG patterns: importance of their recognition and clinical significance." Arquivos de Neuro-Psiquiatria 70, no. 2 (February 2012): 145–51. http://dx.doi.org/10.1590/s0004-282x2012000200014.

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Periodic electroencephalographic (EEG) patterns consist of discharges usually epileptiform in appearance, which occur at regular intervals, in critical patients. They are commonly classified as periodic lateralized epileptiform discharges (PLEDs), bilateral independent PLEDs or BIPLEDs, generalized epileptiform discharges (GPEDs) and triphasic waves. Stimulus-induced rhythmic, periodic or ictal discharges (SIRPIDs) are peculiar EEG patterns, which may be present as periodic discharges. The aim of this study is to make a review of the periodic EEG patterns, emphasizing the importance of their recognition and clinical significance. The clinical significance of the periodic EEG patterns is uncertain, it is related to a variety of etiologies, and many authors suggest that these patterns are unequivocally epileptogenic in some cases. Their recognition and classification are important to establish an accurate correlation between clinical, neurological, laboratorial and neuroimaging data with the EEG results.

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Dasgupta, Abhijit, Losiana Nayak, Ritankar Das, Debasis Basu, Preetam Chandra, and Rajat K. De. "Pattern and Rule Mining for Identifying Signatures of Epileptic Patients from Clinical EEG Data." Fundamenta Informaticae 176, no. 2 (December 18, 2020): 141–66. http://dx.doi.org/10.3233/fi-2020-1968.

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Epilepsy is a neurological condition of human being, mostly treated based on the patients’ seizure symptoms, often recorded over multiple visits to a health-care facility. The lengthy time-consuming process of obtaining multiple recordings creates an obstacle in detecting epileptic patients in real time. An epileptic signature validated over EEG data of multiple similar kinds of epilepsy cases will haste the decision-making process of clinicians. In this paper, we have identified EEG data derived signatures for differentiating epileptic patients from normal individuals. Here we define the signatures with the help of various machine learning techniques, viz., feature selection and classification, pattern mining, and fuzzy rule mining. These signatures will add confidence to the decision-making process for detecting epileptic patients. Moreover, we define separate signatures by incorporating few demographic features like gender and age. Such signatures may aid the clinicians with the generalized epileptic signature in case of complex decisions.

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Luccas, Francisco José Carchedi, Thalita Bártolo, Nayara Lucio da Silva, and Barbara Cavenaghi. "Clinical electroencephalogram (EEG) evaluation is improved by the amplitude asymmetry index." Arquivos de Neuro-Psiquiatria 74, no. 7 (July 2016): 536–43. http://dx.doi.org/10.1590/0004-282x20160082.

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ABSTRACT Cerebral hemispheres, although similar, are neither completely symmetrical in structure nor equivalent in function. EEG asymmetry studies have been directed more to frequency than to amplitude analysis. Objective Better definition of normal amplitude asymmetry values on the classical EEG frequency bands. Results EEG amplitude asymmetry index (AAI) is physiologically low in normal adults, differences usually lesser than 7%. Conclusion Persistent or intermittent amplitude asymmetry regional differences higher than 7% may be suggestive of pathology after adequate correlation with clinical data and EEG classical visual analysis.

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Buzzi, MG, C. Tassorelli, and G. Nappi. "Peripheral and Central Activation of Trigeminal Pain Pathways in Migraine: Data From Experimental Animal Models." Cephalalgia 23, no. 1_suppl (May 2003): 1–4. http://dx.doi.org/10.1046/j.1468-2982.23.s1.1.x.

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EEG-studies in migraine in the last decade has contributed modestly to the understanding of headache pathogenesis. Headache patient groups seem to have increased EEG responses to photic stimulation, but a useful biological marker for migraine in single patients has not been found. In future EEG and QEEG studies we recommend to use follow-up designs and record several EEGs across the migraine cycle. It is also important to use a blinded study design in order to avoid selection bias. A clinical EEG should be performed in patients with acute headache attacks when either epilepsy, basilar migraine, migraine with prolonged aura or alternating hemiplegia is suspected. Unequivocal epileptiform abnormalities usually suggest a diagnosis of epilepsy. In children with occipital spike-wave activity the probable diagnosis is childhood epilepsy with occipital paroxysms (CEOP). The final diagnosis of either an epilepsy syndrome or migraine must be mainly based on a clinical judgement.

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Kennedy, Ashleigh, and Jordan Hassin. "EEG Markers of Cognitive Engagement." Neurology 93, no. 14 Supplement 1 (September 30, 2019): S3.1—S3. http://dx.doi.org/10.1212/01.wnl.0000580848.54563.2d.

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ObjectiveThe purpose of this study was to use portable electroencephalography and qualitative assessments to characterize cognitive changes associated with perceived increase in mental load and to identify markers of mental fatigue in these individuals.BackgroundThe ability to focus on cognitive tasks impacts everything from our social interactions to our success in the classroom or workplace. Concussion negatively impacts the ability to focus and causes patients to experience signs of mental fatigue more quickly than those without concussion. The mechanisms behind these changes are still not well understood.Design/MethodsFifteen concussion patients and fifteen age-matched controls were recruited to participate in this study. Participants performed two, thirty-minute testing sessions spaced 1 month apart. In each session, participants performed 8 cognitive tasks eliciting varying levels of cognitive activity. Cognitive activity was quantitatively assessed using a MUSE non-invasive EEG headset. These data were compared to a perceived level of cognitive activity determined by the individual using the Klepsh et al (2017) cognitive engagement scale and mental fatigue assessed by the Mental Fatigue Scale (Johansson2014).ResultsThe results demonstrate that frequency based EEG changes correlated with decreased ability to focus on the cognitive task and with perceived cognitive fatigue in both concussion patients and healthy controls.ConclusionsFuture studies should utilize the same methods to monitor cognitive activity differences during daily functional living.

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Gerla, V., M. Murgas, V. D. Radisavljevic, L. Lhotska, and V. Krajca. "25. Incremental learning in the task of eeg data classification." Clinical Neurophysiology 125, no. 5 (May 2014): e32-e33. http://dx.doi.org/10.1016/j.clinph.2013.12.063.

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de Munck, Jan C. "A novel correlation analysis of co-registered EEG–fMRI data." Clinical Neurophysiology 119, no. 12 (December 2008): 2671–72. http://dx.doi.org/10.1016/j.clinph.2008.09.009.

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Ratnayake, Chathri, Ramja Kokulan, and Patrick Carney. "115 Is MBS restriction on EEGs truly beneficial? Retrospective observational analysis of EEG referral patterns." Journal of Neurology, Neurosurgery & Psychiatry 90, e7 (July 2019): A37.2—A37. http://dx.doi.org/10.1136/jnnp-2019-anzan.102.

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IntroductionThe November 2018 Medicare Benefits Schedule (MBS) Taskforce draft report recommends standardised national referral form for routine electroencephalogram (EEG) requests to prevent low value clinician referrals. MBS defines twelve clinical presentations where routine EEG is of relatively low diagnostic value. We aim to identify proportion of MBS defined low diagnostic value EEGs and likely referral patterns.MethodsRetrospective single centre observational study was conducted from January to December 2018. All EEG referrals to a tertiary hospital neurodiagnostic unit were categorised as low or high yield based on MBS recommendation. Sub-group analysis of the low yield group was carried out.ResultsTotal of 1210 EEG referrals were analysed and 5 were excluded from analysis due to insufficient clinical data. Of these referrals 1114 (92.4%) were for high yield indications, 77 (6.4%) for low yield and 14 (1.2%) were indeterminate as to low or high value. Of low yield referrals, 70% were referred for syncope or presyncope and 18.2% for psychogenic nonepileptic seizures. Low yield EEGs were referred by hospital doctors, neurologists and general partitioners by proportions of 37.7%, 33.7% and 28.6% respectively. Four (5.2%) low diagnostic value EEGs were abnormal.ConclusionCurrent clinical practice for EEG referrals is in line with MBS draft review recommendations. We believe addition of a standardised referral form and restricting referrals will have minimal impact on referral quality.

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Zhou, Dingfu, Zhihang Liao, and Rong Chen. "Deep Learning Enabled Diagnosis of Children’s ADHD Based on the Big Data of Video Screen Long-Range EEG." Journal of Healthcare Engineering 2022 (April 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/5222136.

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Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. At the same time, ADHD is prone to coexist with other mental disorders, so the diagnosis of ADHD in children is very important. Electroencephalogram (EEG) is the sum of the electrical activity of local neurons recorded from the extracranial scalp or intracranial. At present, there are two main methods of long-range EEG monitoring commonly used in clinical practice: one is ambulatory EEG monitoring, and the other is long-range video EEG monitoring. The purpose of this study is to summarize the brain electrical activity and clinical characteristics of children with ADHD through the video long-range computer graphics data of children with ADHD and to explore the clinical significance of video long-range EEG in the diagnosis of children with ADHD. For a more effective analysis, this study further processed the video data of long-range computer graphics of children with ADHD and constructed several neural network algorithm models based on deep learning, mainly including fully connected neural network models and two-dimensional convolutional neural networks. Model and long- and short-term memory neural network model. By comparing the recognition effects of these several algorithms, find the appropriate recognition algorithm to improve the accuracy and then establish a recognition method for the diagnosis of children’s ADHD based on deep learning long-range EEG big data. Finally, it is concluded that long-term video EEG can analyze the EEG relationship of children with ADHD and provide a diagnostic basis for the diagnosis of ADHD.

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Beumer, Steven, Paul Boon, Debby C. W. Klooster, Raymond van Ee, Evelien Carrette, Maarten M. Paulides, and Rob M. C. Mestrom. "Personalized tDCS for Focal Epilepsy—A Narrative Review: A Data-Driven Workflow Based on Imaging and EEG Data." Brain Sciences 12, no. 5 (May 7, 2022): 610. http://dx.doi.org/10.3390/brainsci12050610.

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Conventional transcranial electric stimulation(tES) using standard anatomical positions for the electrodes and standard stimulation currents is frequently not sufficiently selective in targeting and reaching specific brain locations, leading to suboptimal application of electric fields. Recent advancements in in vivo electric field characterization may enable clinical researchers to derive better relationships between the electric field strength and the clinical results. Subject-specific electric field simulations could lead to improved electrode placement and more efficient treatments. Through this narrative review, we present a processing workflow to personalize tES for focal epilepsy, for which there is a clear cortical target to stimulate. The workflow utilizes clinical imaging and electroencephalography data and enables us to relate the simulated fields to clinical outcomes. We review and analyze the relevant literature for the processing steps in the workflow, which are the following: tissue segmentation, source localization, and stimulation optimization. In addition, we identify shortcomings and ongoing trends with regard to, for example, segmentation quality and tissue conductivity measurements. The presented processing steps result in personalized tES based on metrics like focality and field strength, which allow for correlation with clinical outcomes.

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Holmes, Gregory L. "What is more harmful, seizures or epileptic EEG abnormalities? Is there any clinical data?" Epileptic Disorders 16, NS1 (October 2014): 12–22. http://dx.doi.org/10.1684/epd.2014.0686.

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Tedrus, Gloria MAS, Elizardo Nogueira, and Mariana Almeida Vidal. "Elderly patients with nonconvulsive status epilepticus: Clinical-EEG data, hospital mortality, STESS and EMSE." Seizure 94 (January 2022): 18–22. http://dx.doi.org/10.1016/j.seizure.2021.11.004.

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Ma, Junshui, Shubing Wang, Richard Raubertas, and Vladimir Svetnik. "Statistical methods to estimate treatment effects from multichannel electroencephalography (EEG) data in clinical trials." Journal of Neuroscience Methods 190, no. 2 (July 2010): 248–57. http://dx.doi.org/10.1016/j.jneumeth.2010.05.013.

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