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Journal articles on the topic "EEG, electroencephalogram":
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Shafait, Saima, Wasim Alamgir, Imran Ahmad, Saeed Arif, Jahanzeb Liaqat, and Asif Hashmat. "A STUDY ON COMPARATIVE YIELDS OF STANDARD SHORT TERM ELECTROENCEPHALOGRAM AND LONG TERM ELECTROENCEPHALOGRAM RECORDING IN SUSPECTED EPILEPSY PATIENTS." PAFMJ 71, no. 5 (October 31, 2021): 1727–31. http://dx.doi.org/10.51253/pafmj.v71i5.5921.
Objective: To compare the yield of interictal epileptiform discharges on prolonged (1-2 hours) electroencephalogram (EEG) as compared to standard routine (30 minutes) electroencephalogram (EEG).
Study Design: Comparative observational study.
Place and Duration of Study: Pak Emirates Military Hospital, Rawalpindi from Oct 2019 to Sep 2020.
Methodology: A total of 364 outdoor patients with suspected epilepsy were recruited for the study. Out of these 55 electroencephalograms were excluded after applying exclusion criteria and 309 were included for final analysis. Electro-encephalograms were recorded using a 10-20 international system of electrode placement. The duration of each standard electroencephalogram was 30 minutes. It was followed by recording for an extended period of 60 minutes at least. The time to the appearance of the first abnormal interictal epileptiform discharge was noted. For analytical purposes, epileptiform discharges were classified as “early” if they appeared within the first 30 minutes and as “late” if appeared afterward. All electro-encephalograms were evaluated independently by two neurologists.
Results: A total of 309 electroencephalograms were included for final analysis. Interictal epileptiform discharges were seen in 48 (15.6%) recordings. The mean time to appearance of first interictal epileptiform discharge was 14.6 ± 19.09 minutes. In 36 (11.7%) cases, discharges appeared early (within the first 30 minutes) whereas in the remaining 12 (3.9%) cases, discharges appeared late. This translates into a 33% increase in the diagnostic yield of electroencephalogram with an extended period of recording.
Conclusion: Extending the electroencephalogram recording time results in a significantly better diagnostic yield of outdoor electroencephalogram.
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Duarte-Celada, Walter, Samathorn Thakolwiboon, Jie Pan, Tulio Bueso, and Jannatul Ferdous. "Cefepime-induced non-convulsive status epilepticus." Southwest Respiratory and Critical Care Chronicles 12, no. 50 (January 29, 2024): 38–40. http://dx.doi.org/10.12746/swrccc.v12i50.1277.
Cefepime-induced non-convulsive status epilepticus (NCSE) can develop in patients with advanced age, renal impairment, and previous central nervous system disorders. Its clinical presentation varies from confusion, mutism, and decreased level of consciousness to coma. The typical electroencephalogram (EEG) findings are generalized spike and wave discharges of 1-3 Hz. We present a case series of 4 patients with cefepime-induced NCSE, including the clinical presentation and EEG findings. Electroencephalograms should be part of the workup of acute confusional state in patients on this antibiotic, and physicians should be aware of this uncommon complication.
Keywords: Non-convulsive status epilepticus, cefepime, confusion, mutism, electroencephalogram.
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Golomolzina, Diana Rashidovna, Maxim Alexandrovich Gorodnichev, Evgeny Andreevich Levin, Alexander Nikolaevich Savostyanov, Ekaterina Pavlovna Yablokova, Arthur C. Tsai, Mikhail Sergeevich Zaleshin, et al. "Advanced Electroencephalogram Processing." International Journal of E-Health and Medical Communications 5, no. 2 (April 2014): 49–69. http://dx.doi.org/10.4018/ijehmc.2014040103.
The study of electroencephalography (EEG) data can involve independent component analysis and further clustering of the components according to relation of the components to certain processes in a brain or to external sources of electricity such as muscular motion impulses, electrical fields inducted by power mains, electrostatic discharges, etc. At present, known methods for clustering of components are costly because require additional measurements with magnetic-resonance imaging (MRI), for example, or have accuracy restrictions if only EEG data is analyzed. A new method and algorithm for automatic clustering of physiologically similar but statistically independent EEG components is described in this paper. Developed clustering algorithm has been compared with algorithms implemented in the EEGLab toolbox. The paper contains results of algorithms testing on real EEG data obtained under two experimental tasks: voluntary movement control under conditions of stop-signal paradigm and syntactical error recognition in written sentences. The experimental evaluation demonstrated more than 90% correspondence between the results of automatic clustering and clustering made by an expert physiologist.
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Beer, Nicole A. M. de, Willem L. van Meurs, Marco B. M. Grit, Michael L. Good, and Dietrich Gravenstein. "Educational simulation of the electroencephalogram (EEG)." Technology and Health Care 9, no. 3 (April 1, 2001): 237–56. http://dx.doi.org/10.3233/thc-2001-9302.
Eklöf-Areskog, G., J. P. Aronsson, and L. Petersson. "P24.12 Melatonin for sleep-electroencephalogram (EEG)." Clinical Neurophysiology 122 (June 2011): S172. http://dx.doi.org/10.1016/s1388-2457(11)60619-6.
AbstractSeizures are common in critically ill patients. Electroencephalogram (EEG) is a tool that enables clinicians to provide continuous brain monitoring and to guide treatment decisions—brain telemetry. EEG monitoring has particular utility in the intensive care unit as most seizures in this setting are nonconvulsive. Despite the increased use of EEG monitoring in the critical care unit, it remains underutilized. In this review, we summarize the utility of EEG and different EEG modalities to monitor patients in the critical care setting.
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Fonseca, Lineu C., Glória M. A. S. Tedrus, Marcelo G. Chiodi, Jaciara Näf Cerqueira, and Josiane M. F. Tonelotto. "Quantitative EEG in children with learning disabilities: analysis of band power." Arquivos de Neuro-Psiquiatria 64, no. 2b (June 2006): 376–81. http://dx.doi.org/10.1590/s0004-282x2006000300005.
In order to better understand the mechanisms of learning disabilities it is important to evaluate the electroencephalogram parameters and their relation to the results of the Wechsler Intelligence Scale. Thirty-six children with complaints of learning disability were studied. Electroencephalograms were carried out while awake and resting, and the values for absolute and relative powers calculated. The results were compared with those of 36 healthy children paired with respect to age, gender and maternal scholastic level. In the group with learning disabilities, the absolute (in the delta, theta and alpha 1 bands) and relative (theta) power values were higher and the relative power alpha 2 value significantly lower at the majority of the electrodes in relation to the control group. There was a high positive correlation in the children with learning disabilities between the relative power alpha 2 and the verbal, performance and total IQ values. These quantitative electroencephalogram findings in children with learning disabilities have a clear relation with psychological measurements and could be due to brain immaturity.
Objective Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Methods Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. Results The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p < 0.05) but not in the occipital area. The measurement of test-retest reliability (n = 8) using the intra-class correlation coefficient was good with r1 = 0.73 ( p = 0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76 ± 4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Conclusion Entropy-based analytical methods identified enhancement or “normalization” of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.
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Cappellari, Alberto M. "Normal Neonatal Electroencephalogram at a Glance." Journal of Neonatology 34, no. 4 (December 2020): 236–40. http://dx.doi.org/10.1177/0973217920977532.
Interpreting neonatal electroencephalogram (EEG) presents a challenge owing to rapid evolution of EEG patterns occurring during brain maturation in the neonatal period and rich variety of normal patterns of EEG activity, which is difficult to categorize completely. Furthermore, the description of some aspects during maturation varies in different studies. Neonatal EEG is unfamiliar to most neurologists, and its interpretation requires knowledge of the physiological markers of electrogenesis maturation. The purpose of this review was to provide health-care professionals in the neonatal intensive care unit with guidance on the more common normal maturational features of the neonatal EEG. A simplified layout with the essential elements of normal neonatal EEG is included.
Duta, Mihaela D. "The study of vigilance using neural networks analysis of EEG." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301454.
Lee, Pamela Wen-Hsin. "Mutual information derived functional connectivity of the electroencephalogram (EEG)." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/219.
Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis.
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Mathew, Blesy Anu. "ENTROPY OF ELECTROENCEPHALOGRAM (EEG) SIGNALS CHANGES WITH SLEEP STATE." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/203.
We hypothesized that temporal features of EEG are altered in sleep apnea subjects comparedto normal subjects. The initial aim was to develop a measure to discriminate sleep stages innormals. The longer-term goal was to apply these methods to identify differences in EEGactivity in sleep apnea subjects from normals. We analyzed the C3A2 EEG and anelectrooculogram (EOG) recorded from 9 normal adults awake and in rapid eye movement(REM) and non-REM sleep. The EEG signals were filtered to remove EOG contamination. Twomeasures of the irregularity of EEG signals, Sample Entropy (SpEn) and Tsallis Entropy, wereevaluated for their ability to discriminate sleep stages. SpEn changes with sleep state, beinglargest in Wake. Stage 3/4 had the smallest SpEn (0.57??0.11) normalized to Wake values,followed by Stage 2 (0.72??0.09), REM (0.75??0.1) and Stage 1 (0.89??0.05). This pattern wasconsistent in all the polysomnogram records analyzed. Similar pattern was observed in leadO1A2 as well. We conclude that SpEn may be useful as part of a montage for assessing sleepstate. We analyzed data from sleep apnea subjects having obstructive and central apnea eventsand have made some preliminary observations; the SpEn values were more similar across sleepstages and also high correlation with oxygen saturation was observed.
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Riddington, Edward Peter. "Automated interpretation of the background EEG using fuzzy logic." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1109.
A new framework is described for managing uncertainty and for dealing with artefact corruption to introduce objectivity in the interpretation of the electroencephalogram (EEG). Conventionally, EEG interpretation is time consuming and subjective, and is known to show significant inter- and intra-personnel variation. A need thus exists to automate the interpretation of the EEG to provide a more consistent and efficient assessment. However, automated analysis of EEGs by computers is complicated by two major factors. The difficulty of adequately capturing in machine form, the skills and subjective expertise of the experienced electroencephalbgrapher, and the lack of a reliable means of dealing with the range of EEG artefacts (signal contamination). In this thesis, a new framework is described which introduces objectivity in two important outcomes of clinical evaluation of the EEG, namely, the clinical factual report and the clinical 'conclusion', by capturing the subjective expertise of the electroencephalographer and dealing with the problem of artefact corruption. The framework is separated into two stages .to assist piecewise optimisation and to cater for different requirements. The first stage, 'quantitative analysis', relies on novel digital signal processing algorithms and cluster analysis techniques to reduce data and identify and describe background activities in the EEG. To deal with artefact corruption, an artefact removal strategy, based on new reUable techniques for artefact identification is used to ensure that artefact-free activities only are used in the analysis. The outcome is a quantitative analysis, which efficiently describes the background activity in the record, and can support future clinical investigations in neurophysiology. In clinical practice, many of the EEG features are described by the clinicians in natural language terms, such as very high, extremely irregular, somewhat abnormal etc. The second stage of the framework, 'qualitative analysis', captures the subjectivity and linguistic uncertainty expressed.by the clinical experts, using novel, intelligent models, based on fuzzy logic, to provide an analysis closely comparable to the clinical interpretation made in practice. The outcome of this stage is an EEG report with qualitative descriptions to complement the quantitative analysis. The system was evaluated using EEG records from 1 patient with Alzheimer's disease and 2 age-matched normal controls for the factual report, and 3 patients with Alzheimer's disease and 7 age-matched nonnal controls for the 'conclusion'. Good agreement was found between factual reports produced by the system and factual reports produced by qualified clinicians. Further, the 'conclusion' produced by the system achieved 100% discrimination between the two subject groups. After a thorough evaluation, the system should significantly aid the process of EEG interpretation and diagnosis.
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D'ROZARIO, Angela Louise. "Electroencephalogram (EEG) biomarkers of neurobehavioural dysfunction in obstructive sleep apnea." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/9886.
Obstructive sleep apnea (OSA) affects an estimated 2-4% of middle–aged adults yet we are still exploring how best to delineate the neurophysiological deficits that accompany this disorder. Untreated OSA leads to an increased risk of motor vehicle accidents. Traditional polysomnographic (PSG) metrics do not consistently correlate with daytime functioning. There is a clinical need for simple biomarkers to identify individuals susceptible to OSA-related cognitive deficits. There is a close relationship between EEG-based changes in brain activity and daytime function in healthy sleepers. No studies have explored quantitative EEG (qEEG) biomarkers during baseline sleep and resting wakefulness (baseline) as correlates of waking neurobehavioural performance during extended wakefulness in OSA. The aims of the thesis were 1) to identify qEEG biomarkers of neurobehavioural dysfunction and sleepiness in OSA and controls during 40-hours (h) of extended wakefulness, and 2) to develop and validate automated EEG artefact processing methods for subsequent qEEG analysis of waking and sleep EEG. EEG biomarkers were derived using conventional power spectral analysis and a novel qEEG analysis technique, detrended fluctuation analysis (DFA). This study showed that wake qEEG markers significantly correlated with impaired performance and increased sleepiness across 40-h of extended wakefulness in both groups. Baseline waking measures of the DFA scaling exponent, but not power spectra, were associated with impaired simulated driving after 24-h awake in OSA. Furthermore, OSA patients with greater EEG slowing during REM sleep showed a marked decline in performance after 24-h awake. These key findings highlight the potential utility of qEEG analysis to yield simple biomarkers of neurobehavioural impairment and sleepiness. Automated EEG artefact processing methods for resting awake and PSG recordings were developed and validated against a reference-standard of manual artefact recognition as part of this study. These proven artefact processing methods will be pivotal for exploring qEEG biomarkers in future studies.
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Löfhede, Johan. "Classification of Burst and Suppression in the Neonatal EEG." Licentiate thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3448.
The brain requires a continuous supply of oxygen and even a short period ofreduced oxygen supply risks severe and lifelong consequences for theaffected individual. The delivery is a vulnerable period for a baby who mayexperience for example hypoxia (lack of oxygen) that can damage the brain.Babies who experience problems are placed in an intensive care unit wheretheir vital signs are monitored, but there is no reliable way to monitor thebrain directly. Monitoring the brain would provide valuable informationabout the processes going on in it and could influence the treatment and helpto improve the quality of neonatal care. The scope of this project is todevelop methods that eventually can be put together to form a monitoringsystem for the brain that can function as decision-support for the physician incharge of treating the patient.The specific technical problem that is the topic of this thesis is detection ofburst and suppression in the electroencephalogram (EEG) signal. The thesisstarts with a brief description of the brain, with a focus on where the EEGoriginates, what types of activity can be found in this signal and what theymean. The data that have been available for the project are described,followed by the signal processing methods that have been used for preprocessing,and the feature functions that can be used for extracting certaintypes of characteristics from the data are defined. The next section describesclassification methodology and how it can be used for making decisionsbased on combinations of several features extracted from a signal. Theclassification methods Fisher’s Linear Discriminant, Neural Networks andSupport Vector Machines are described and are finally compared with respectto their ability to discriminate between burst and suppression. An experimentwith different combinations of features in the classification has also beencarried out. The results show similar results for the three methods but it canbe seen that the SVM is the best method with respect to handling multiplefeatures.
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Murrell, Joanna. "Spontaneous EEG changes in the equine surgical patient." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340352.
Phase Synchrony (PS) and coherence analyses of stochastic time series - tools to discover brain tissue pathways traveled by electrical signals - are considered for the specific purpose of processing of the electroencephalogram (EEG).
We propose the Phase Synchrony Processor (PSP), as a tool for implementing phase synchrony analysis, and examine its properties on the basis of known signals. Long observation times and wide filter bandwidths can decrease bias in PS estimates. The value of PS is affected by the difference in frequency of the sequences being analyzed and can be related to that frequency difference by the periodic sinc function.
PS analysis of the EEG shows that the average PS is higher - for a number of electrode pairs - for non-ADHD than for ADHD participants. The difference is more pronounced in the δ rhythm (0-3 Hz) and in the γ rhythm (30-50 Hz) PS. The Euclidean classifier with electrode masking yields 66 % correct classification on average for ADHD and non-ADHD subjects using the δ and γ1 rhythms.
We observed that the average γ1 rhythm PS is higher for the eyes closed condition than for the eyes open condition. The latter may potentially be used for vigilance monitoring. The Euclidean discriminator with electrode masking shows an average percentage of correct classification of 78 % between the eyes open and eyes closed subject conditions.
We develop a model for a pair of EEG electrodes and a model-based MS coherence estimator aimed at processing short (i.e. 20 samples) EEG frames. We verify that EEG sequences can be modeled as AR(3) processes degraded by additive white noise with an average SNR of approximately 11-12 dB.
Application of the MS coherence estimator to the EEG suggests that MS coherence is generally higher for non-ADHD individuals than for ADHD participants when evaluated for the θ rhythm of EEG. Also, MS coherence is consistently higher for ADHD subjects than for the majority of non-ADHD individuals when computed for the low end of the δ rhythm (i.e. below 1 Hz).
ADHD produces more measurable effects in the frontal lobe EEG and for participants performing attention intensive tasks. Ph. D.
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Ascolani, Gianluca. "EEG, Alpha Waves and Coherence." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc28389/.
This thesis addresses some theoretical issues generated by the results of recent analysis of EEG time series proving the brain dynamics are driven by abrupt changes making them depart from the ordinary Poisson condition. These changes are renewal, unpredictable and non-ergodic. We refer to them as crucial events. How is it possible that this form of randomness be compatible with the generation of waves, for instance alpha waves, whose observation seems to suggest the opposite view the brain is characterized by surprisingly extended coherence? To shed light into this apparently irretrievable contradiction we propose a model based on a generalized form of Langevin equation under the influence of a periodic stimulus. We assume that there exist two different forms of time, a subjective form compatible with Poisson statistical physical and an objective form that is accessible to experimental observation. The transition from the former to the latter form is determined by the brain dynamics interpreted as emerging from the cooperative interaction among many units that, in the absence of cooperation would generate Poisson fluctuations. We call natural time the brain internal time and we make the assumption that in the natural time representation the time evolution of the EEG variable y(t) is determined by a Langevin equation perturbed by a periodic process that in this time representation is hardly distinguishable from an erratic process. We show that the representation of this random process in the experimental time scale is characterized by a surprisingly extended coherence. We show that this model generates a sequence of damped oscillations with a time behavior that is remarkably similar to that derived from the analysis of real EEG's. The main result of this research work is that the existence of crucial events is not incompatible with the alpha wave coherence. In addition to this important result, we find another result that may help our group, or any other research group working on the analysis of brain's dynamics, to prove or to disprove the existence of crucial events. We study the diffusion process generated by fluctuations emerging from the same model after filtering out the alpha coherence, and we study the recursion to the origin. We study the survival probability of this process, namely the probability that up to a given time no re-crossing of the origin occurs. We find that this is an inverse power law with a power that depends on whether or not crucial events exist.
National Institutes of Health (U.S.). Office of Clinical Center Communications, ed. EEG (electroencephalogram). [Bethesda, Md.?]: Clinical Center Communications, National Institutes of Health, 1989.
Kam, Julia W. Y., and Todd C. Handy. Electroencephalogram Recording in Humans. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0006.
This chapter provides an elementary introduction to the theory and practical application of electroencephalogram (EEG) recording for the purpose of studying neurocognitive processes. It is aimed at readers who have had little or no experience in EEG data collection, and would like to gain a better understanding of scientific papers employing this methodology or start their own EEG experiment. We begin with a definition of EEG, and a summary of the strengths and limitations of EEG-based techniques. Following this is a description of the basic theory concerning the cellular mechanisms underlying EEG, as well as two types of data generated by EEG recording. We then present a brief summary of the equipment necessary for EEG data acquisition and important considerations for presentation software. Finally, we provide an overview of the protocol for data acquisition and processing, as well as methods for quantifying both EEG and event-related potentials data.
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Vespa, Paul M. Electroencephalogram monitoring in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0221.
Electroencephalography monitoring provides a method for monitoring brain function, which can complement other forms of monitoring, such as monitoring of intracranial pressure and derived parameters, such as cerebral perfusion pressure. Continuous electroencephalogram (EEG) monitoring can be helpful in seizure detection after brain injury and coma. Seizures can be detected by visual inspection of the raw EEG and/or processed EEG data. Treatment of status epilepticus can be improved by rapid identification and abolition of seizures using continuous EEG. Quantitative EEG can also be used to detect brain ischaemia and seizures, to monitor sedation and aid prognosis.
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Pearl, Phillip L., Jules Beal, Monika Eisermann, Sunita Misra, Perrine Plouin, Solomon L. Moshe, James J. Riviello, Douglas R. Nordli, and Eli M. Mizrahi. Normal EEG in Wakefulness and Sleep. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0007.
Electroencephalogram (EEG) interpretation depends on accurate pattern recognition. One of the first lessons the novice electroencephalographer learns is that EEG pattern interpretation must take into account the patient’s age and the level of vigilance, or state. EEG patterns vary according to central nervous system development and maturation. This process evolves over time, starting with the early development and maturation of the nervous system (an evolution) to a peak of maturity, followed by an involution. Basic differences exist between the ascending (developmental) and descending (involutional) portions of this curve. This chapter discusses pediatric EEG, from the dramatic ontogenic transitioning of the neonate, premature and term, to infants, children, and adolescents.
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Krishnan, Vaishnav, Bernard S. Chang, and Donald L. Schomer. Normal EEG in Wakefulness and Sleep. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0008.
The normal adult electroencephalogram (EEG) is not a singular entity, and recognizing and appreciating the various expressions of a normal EEG is vital for any electroencephalographer. During wakefulness, the posterior dominant rhythm (PDR) must display a frequency within the alpha band, although an absent PDR is not abnormal. A symmetrically slowed PDR, excessive theta activity, or any delta activity during wakefulness is abnormal and a biomarker of encephalopathy. Low-voltage EEGs have been associated with a variety of neuropathological states but are themselves not abnormal. During non-rapid eye movement sleep, a normal EEG will display progressively greater degrees of background slowing and amplitude enhancement, which may or may not be associated with specific sleep-related transients. In contrast, the EEG during rapid eye movement sleep more closely resembles a waking EEG (“desynchronized”) in amplitude and background frequencies. Across both wakefulness and sleep, significant asymmetries in background frequencies and amplitude are abnormal.
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Butkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.
This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.
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Gaitanis, John, Phillip L. Pearl, and Howard Goodkin. The EEG in Degenerative Disorders of the Central Nervous System. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0013.
Nervous system alterations can occur at any stage of prenatal or postnatal development. Any of these derangements, whether environmental or genetic, will affect electrical transmission, causing electroencephalogram (EEG) alteration and possibly epilepsy. Genetic insults may be multisystemic (for example, neurocutaneous syndromes) or affect only the brain. Gene mutations account for inborn errors of metabolism, channelopathies, brain malformations, and impaired synaptogenesis. Inborn errors of metabolism cause seizures and EEG abnormalities through a variety of mechanisms, including disrupted energy metabolism (mitochondrial disorders, glucose transporter defect), neuronal toxicity (amino and organic acidopathies), impaired neuronal function (lysosomal and peroxisomal disorders), alteration of neurotransmitter systems (nonketotic hyperglycinemia), and vitamin and co-factor dependency (pyridoxine-dependent seizures). Environmental causes of perinatal brain injury often result in motor or intellectual impairment (cerebral palsy). Multiple proposed etiologies exist for autism, many focusing on synaptic development. This chapter reviews the EEG findings associated with this myriad of pathologies occurring in childhood.
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Koutroumanidis, Michalis, and Robin Howard. Encephalopathy, central nervous system infections, and coma. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0032.
This chapter provides an overview of the indications for and the diagnostic and prognostic value of acute video-electroencephalogram (EEG) and continuous video-EEG monitoring in patients with encephalopathies, encephalitides, and coma. Particular emphasis is placed on the detection of non-convulsive seizures and non-convulsive status epilepticus secondary to acute and sub-acute cerebral insults, including post-cardiac arrest hypoxic-ischaemic brain injury, and on the related pitfalls and uncertainties. It also discusses key technical aspects of the EEG recording, including artefact identification and limitation, timing and type of external stimulation and assessment of EEG reactivity, and highlights the main relevant pitfalls. Finally, it explores the role of evoked potentials (EPs) in outcome prediction and the value of Cognitive EPs and quantitative EEG in the assessment of chronic disorders of consciousness.
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Luginbühl, Martin, and Arvi Yli-Hankala. Assessment of the components of anaesthesia. Edited by Antony R. Wilkes and Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0026.
In modern anaesthesia practice, hypnotic drugs, opioids, and neuromuscular blocking agents (NMBAs) are combined. The introduction of NMBAs in particular substantially increased the risk of awareness and recall during general anaesthesia. Hypnotic drugs such as propofol and volatile anaesthetics act through GABAA receptors and have typical effects on the electroencephalogram (EEG). During increasing concentrations of these pharmaceuticals, the EEG desynchronization is followed by gradual synchronization, slowing frequency, and increasing amplitude of EEG, thereafter EEG suppressions (burst suppression), and, finally, isoelectric EEG. Hypnotic depth monitors such as the Bispectral Index™, Entropy™, and Narcotrend® are based on quantitative EEG analysis and translate these changes into numbers between 100 and 0. Although they are good predictors of wakefulness and deep anaesthesia, their usefulness in prevention of awareness and recall has been challenged, especially when inhalation anaesthetics are used. External and patient-related artifacts such as epileptiform discharges and frontal electromyography (EMG) affect the signal so their readings need careful interpretation. Their use is recommended in patients at increased risk of awareness and recall and in patients under total intravenous anaesthesia. Monitors of analgesia and nociception are not established in clinical practice but mostly remain experimental although some are commercially available. Some use EEG changes induced by noxious stimulation (EEG arousal) or quantify the frontal EMG in relation to EEG, while others are based on the sympathoadrenergic stress response. Various other devices are also discussed in this chapter.
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Ewen, Joshua B., and Sándor Beniczky. Validating Biomarkers and Diagnostic Tests in Clinical Neurophysiology. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0009.
There has been an explosion in the development of electroencephalogram (EEG)-based biomarkers and clinical tests. This upsurge is likely due to an increase in therapies rooted in biological mechanisms rather than behavioral descriptions, as well as to the democratization of computational power and the lower cost of EEG compared with competing methodologies. This increase in motivation and opportunity demands an increased responsibility for proper validation studies. Fields including laboratory medicine and psychometrics have paved the way for rigorous validation methodology. This chapter reviews a systematic methodology for biomarker/clinical test validation, translating approaches from other fields to the specific characteristics of EEG-based metrics. A checklist is provided to help readers design high-quality diagnostic validation studies of EEG-based biomarkers.
Charman, Tony, Susan Hepburn, Moira Lewis, Moira Lewis, Amanda Steiner, Sally J. Rogers, Annemarie Elburg, et al. "Electroencephalogram (EEG)." In Encyclopedia of Autism Spectrum Disorders, 1067–68. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_720.
Luhmann, Heiko J. "EEG (Electroencephalogram)." In Encyclopedia of Sciences and Religions, 696. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_200675.
Vogel, Friedrich. "The Human EEG: General Aspects." In Genetics and the Electroencephalogram, 7–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57040-7_2.
Vogel, Friedrich. "Event-Related (Evoked) EEG Potentials." In Genetics and the Electroencephalogram, 93–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57040-7_5.
Vogel, Friedrich. "Family Studies on the Normal EEG." In Genetics and the Electroencephalogram, 55–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57040-7_4.
Vogel, Friedrich. "The EEG in Hereditary Anomalies and Diseases." In Genetics and the Electroencephalogram, 197–214. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57040-7_8.
Siuly, Siuly, Yan Li, and Yanchun Zhang. "Electroencephalogram (EEG) and Its Background." In Health Information Science, 3–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47653-7_1.
Conference papers on the topic "EEG, electroencephalogram":
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Ashby, Corey, Amit Bhatia, Francesco Tenore, and Jacob Vogelstein. "Low-cost electroencephalogram (EEG) based authentication." In 5th International IEEE/EMBS Conference on Neural Engineering (NER 2011). IEEE, 2011. http://dx.doi.org/10.1109/ner.2011.5910581.
Alhammadi, Maitha, Bara'Ah Othman, Sara Rashid Bani Rasheed, Talal Bonny, Wafaa Al Nassan, and Khaled Obaideen. "Cursor Control Using electroencephalogram (EEG) Technology." In 2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA). IEEE, 2022. http://dx.doi.org/10.1109/icecta57148.2022.9990531.
Itsueli, Aloaye E., Jonathan D. N. Kamba, Jeremie O. K. Kamba, and R. Alba-Flores. "Drone Control Using Electroencephalogram (EEG) Signals." In SoutheastCon 2022. IEEE, 2022. http://dx.doi.org/10.1109/southeastcon48659.2022.9764002.
Xu Huang, Salahiddin Altahat, Dat Tran, and Dharmendra Sharma. "Human identification with electroencephalogram (EEG) signal processing." In 2012 International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2012. http://dx.doi.org/10.1109/iscit.2012.6380841.
Gurkan, Hakan, Umit Guz, and B. Siddik Yarman. "A novel Electroencephalogram (EEG) data compression technique." In 2008 IEEE 16th Signal Processing, Communication and Applications Conference (SIU). IEEE, 2008. http://dx.doi.org/10.1109/siu.2008.4632749.
Mohammad, Khader, Saleem Hamo, Mohammad Abbas, and Maen Mohammad. "Emotion Recognition Based on Electroencephalogram (EEG) Signals." In 2023 International Conference on Microelectronics (ICM). IEEE, 2023. http://dx.doi.org/10.1109/icm60448.2023.10378884.
Hendrawan, Muhammad Afif, Ulla Delfana Rosiani, and Arwin Datumaya Wahyudi Sumari. "Single Channel Electroencephalogram (EEG) Based Biometric System." In 2022 IEEE 8th Information Technology International Seminar (ITIS). IEEE, 2022. http://dx.doi.org/10.1109/itis57155.2022.10010103.
Demir, Andac, Toshiaki Koike-Akino, Ye Wang, and Deniz Erdogmus. "EEG-GAT: Graph Attention Networks for Classification of Electroencephalogram (EEG) Signals." In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9871984.
Demir, Andac, Toshiaki Koike-Akino, Ye Wang, Masaki Haruna, and Deniz Erdogmus. "EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630194.
Zhang, Yuge, Qin Wang, Zheng Yang Chin, and Kai Keng Ang. "Investigating different stress-relief methods using Electroencephalogram (EEG)." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175900.
