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PubMed1998

Value of preoperative EEG for carotid endarterectomy.

PURPOSE: This study was designed to determine whether the preoperative, baseline electroencephalogram (EEG) can be used for intraoperative decision making during carotid endarterectomy, and to identify circumstances where the EEG can be eliminated. METHODS: The charts of all patients undergoing carotid endartectomy at the authors' institution from June 1991 to May 1995 were reviewed to identify those patients that had adequate pre- and intraoperative EEG monitoring. EEGs during 331 carotid endartectomies in 303 patients were coded without knowledge of outcome; primary and secondary endpoints were EEG changes with clamping and clinical outcome, respectively. RESULTS: The incidence of mortality and major neurological morbidity was 1.8%. Baseline-EEGs were abnormal in 105 patients (32%). Whereas baseline-EEG changes were highly predictive of EEG changes after anesthetic induction (P < .0001), they were not predictive of EEG changes with clamping or of clinical outcome. Prior stroke (CVA) predicted abnormal baseline-EEGs (P < .0001) and abnormal post-anesthetic EEGs (P < .0001) but did not predict changes with clamping or perioperative CVA. EEG changes with clamping occurred during 18% of operations; such changes were predicted only by contralateral occlusion (P < .0016) and EEG changes during a prior contralateral carotid endartectomy (P < .0001). The only variable that predicted an adverse neurological outcome was the presence of contralateral occlusion, which increased the likelihood of a perioperative neurological event seven-fold (P = .0038). Clinical outcomes in the 57 of 105 patients with abnormal baseline-EEGs and the 49 of 83 with prior CVA who were shunted were not different from those who were not. CONCLUSIONS: baseline-EEG is not of value for the prediction of adverse events during carotid endartectomy and can be eliminated. Because contralateral occlusion is highly predictive of changes with clamping, and patients undergoing a second carotid endartectomy will usually manifest EEG changes identical to those at the first, operative EEG monitoring can also be eliminated from both these circumstances. Finally, prior stroke does not lead to a higher incidence of clamp-induced EEG changes, and thus is not an indication for shunting in and of itself.

Brain Ischemia↗
PubMed2006

Screening of EEG referrals by neurologists leads to improved healthcare resource utilization.

The objective of this study was to determine if screening by a neurologist of all non-neurologist electroencephalogram (EEG) referrals prior to approval reduces the number of inappropriate requests. This retrospective survey included 600 consecutive EEG requisitions referred to the Anaheim Kaiser Permanente Neurodiagnostic Laboratory to rule out epilepsy. Patients with established epilepsy referred for a repeat EEG for management issues were excluded. Three groups of EEG referrals were analyzed. Each group consisted of 200 EEGs (100 pediatric and 100 adult EEGs). The first group was referred directly by non-neurologists, the second group was referred by non-neurologists with scrutiny by a neurologist, and the third group was referred by a neurologist directly. In the pediatric group, the ratio of abnormal EEG vs normal EEG was 1:3.35 in the first group, 1:0.69 in the second group and 1:0.33 in the third group. In the adult group, the ratio of abnormal EEGs vs normal EEGs was 1:2.23 in the first group, 1:0.82 in the second group and 1:0.45 in the third group. In the combined pediatric and adult groups, the ratio of abnormal EEG vs normal EEG was 1:2.70 in the first group, 1:0.75 in the second group and 1:0.39 in the third group. There was a significant difference between the results of the EEGs ordered by non-neurologists directly versus non-neurologists with scrutiny (p=.334, chi-square test). Scrutiny by a neurologist of EEG referrals from non-neurologists led to a reduction in the number of normal EEG results. This suggests that inappropriate EEG requests for non-epileptic patients that yield normal EEG results are significantly reduced with scrutiny. This can help reduce the indiscriminate overuse of EEGs by non-neurologists thereby leading to better utilization of healthcare resources.

California↗
PubMed1996

Quantitative EEG analysis as a supplement to the clinical coma scale RLS85.

BACKGROUND: The aim of the study was to investigate the correlation between EEG indicators and clinical scores based on the RLS85 (Reaction Level Scale 85) in comatose patients. The results of a simple visual assessment of the EEG, using an arbitrary scale with typical EEG patterns, were compared with those obtained by quantitative electroencephalography (qEEG). METHOD: The RLS85 scores were examined in 34 patients with impaired consciousness due to brain tumours, vascular lesions or head injuries. The EEG was recorded shortly before or after the clinical examination. The semiquantitative assessment was made by visual inspection of the tracings, using an arbitrary scale where 12 EEG patterns with increasing proportion of slow activity were displayed. Parallel to the visual analysis, the EEGs were processed by means of EEG spectrum analysis and the power/amplitude in slow frequency bands was used as an indicator. The results were based on correlation between various types of EEG variables and the RLS scores which were obtained in the same patients. RESULTS: The correlation between the visual EEG indicators and coma scores ranged between 0.53 and 0.57 (P < 0.01). As regards the computerised EEG analysis, the correlation between the clinical scores and various EEG spectrum values did not exceed 0.45 (P < 0.01). A higher correlation could be obtained by combining eight EEG variables; the multiple correlation coefficient was then 0.68. CONCLUSIONS: The amount of EEG slow activity is significantly correlated to the RLS85 score. This means that the EEG also provides information on the level and not only on the changes of the coma degree. Surprisingly, the indicators based on quantitative EEG, as used in commercially available instruments, did not give better results than the visual assessment. However, the results of the computerised analysis could be improved using multivariate statistical methods. The study also showed a way to improve communication between the neurophysiologist and clinician by presenting the EEG findings in terms similar to those used in the clinical scales. However, the clinician should be aware of the fact that the "EEG score" and the clinical score is not the same: the intention is to supplement rather than to simulate the clinical observation.

Adolescent↗
PubMed1998

Cognitive decline unlike normal aging is associated with alterations of EEG temporo-spatial characteristics.

The diagnosis of beginning dementia is based mainly on neuropsychological testing. Several measures of EEG spectral composition, coherence and complexity (correlation dimension) have been shown to correspond to cognitive function. Only a few studies have evaluated EEG changes in normal aging, and no quantitative study has addressed changes in EEG during cognitive tasks in demented elderly. In this study the quantitative descriptors of EEGs from 31 demented or cognitively impaired elderly persons, 30 healthy elderly (mean age 69 years) and 35 young controls (mean age 31 years) were compared. The EEGs were recorded during two resting conditions (eyes closed and eyes opened) and two tasks (mental arithmetics and a lexical decision). The goal of the study was to evaluate which temporal and spatial EEG descriptors change with cognitive decline and with normal aging, respectively. Cognitive categories (unimpaired, impaired, demented) were based on Structured Interview for the Diagnosis of Dementia of Alzheimer Type (SIDAM) scores. The EEGs were analysed using adaptive segmentation of continuous EEG, which quantifies the succession of distinct stable topographic voltage patterns (EEG microstates). The main findings were a significant increase in the number of ultra-short EEG microstates and, independently, a reduction in the average duration of EEG microstates in the cognitively impaired and demented patients. In addition, cognitive impairment was associated with a reduction or loss of EEG reactivity normally observed when the resting states with closed and with opened eyes are compared. No alterations in temporal or spatial EEG descriptors were found in normal aging. Cognitive tasks did not add to information already obtained during the resting states. The reduction in EEG microstate duration correlated with loss of cognitive function. Temporo-spatial analysis of EEG therefore is a useful indicator of cortical dysfunction in dementia, correlating with the degree of cognitive impairment. Normal aging seems not to be accompanied by changes in temporo-spatial EEG patterns. The data suggest that fragmentation of the electrophysiological processes underlies cognitive dysfunction in Alzheimer's disease.

Adult↗
PubMed2003

EEG in children with early-onset benign occipital seizure susceptibility syndrome: Panayiotopoulos syndrome.

PURPOSE: We analyzed sequential changes in the localization of EEG foci along with age to identify a specific EEG pattern, and the relation between the clinical manifestations and the EEG pattern in patients with Panayiotopoulos syndrome (PS). METHODS: The subjects were 76 children, who had been followed up >2 years with repeated EEG examinations at 6-month intervals. Analysis of EEG findings included the determination of localization of spike foci, as a function of age, by using cross-sectional data, and the identification of subgroups with homogeneous EEG patterns. Then we compared certain clinical features among these subgroups. RESULTS: In the cross-sectional EEG study, the occipital EEG spike focus was most frequently seen between ages 2 and 5 years. Independent and synchronous frontopolar and occipital spikes (Fp-O spikes) and centroparietotemporal (CPT) EEG spike foci had increased incidences between ages 4 and 7 years, and between ages 6 and 10 years, respectively. We subclassified the 76 patients into the following five subgroups based on the evolutional changes in epileptic EEG foci, which frequently showed shifting, multiplications, and generalization: (a) persistent occipital focus group (O group), (b) Fp-O spikes group (Fp-O pattern group), (c) generalized EEG pattern group, (d) CPT foci group (CPT group), and (e) no epileptic EEG focus group. The Fp-O group showed the latest age at onset of epilepsy. The generalized EEG pattern group had the highest frequency of seizures as well as recurrences of status epilepticus (SE), as well as the longest active seizure period among the five groups. CONCLUSIONS: These results indicated that the EEG foci in most of patients with PS are frequently shifting location, multiplying, and propagating diffusely with age, rather than persistently localizing in the occipital region. In addition, the EEG patterns showed a certain trend and roughly corresponded to certain clinical characteristics. However, the prognosis of the seizures appeared to be favorable regardless of the EEG pattern.

Age Factors↗
PubMed1996

[Age-related correlation between EEG parameters and depth of anesthesia under propofol. Effect of fentanyl].

UNLABELLED: This study was designed to determine the relationship between the electroencephalogram (EEG) and clinical signs of depth of anaesthesia during induction of anaesthesia by slow infusion of propofol (18 mg/kg.h). METHODS: Four groups of 12 patients each were studied (groups I and II: 18-50 years; groups III and IV: > 70 years). Groups II and IV were given 0.15 mg fentanyl before the infusion of propofol was started. The clinical signs recorded were: (1) loss of eyelash reflex; (2) respiratory insufficiency; (3) tolerance to painful stimuli; and (4) intubation. Cardiovascular reactions were documented. The dosage was calculated from the infusion time (time from start of infusion until specific clinical event). Bipolar electrodes were placed at the C4/P4 positions (10-20 placement system) to record the EEG, which was processed by a personal computer (Narkograph) using fast-fourier transformation. The Narkograph calculates multiparametric EEG stages ranging from A to F (according to Kugler) as well as median frequency and spectral-edge frequency 95% (SEF). Stage A represents alpha rhythm, stage F is equivalent to a burst suppression pattern. For statistical analysis a Student t-test was performed. RESULTS: The infusion of propofol led to slowly developing anaesthesia with loss of eyelash reflex followed by loss of pain response, respiratory insufficiency, and intubation. In the younger patients the clinical signs coincided with well-differentiable EEG patterns. Above 70 years of age there were problems in distinguishing the EEG patterns, as there are alterations of the EEG with advanced age. The multiparametric EEG stage calculated by the Narkograph showed a better correlation with the clinical signs than median or SEF. Fentanyl shortened the induction time remarkably: less propofol was needed to achieve corresponding clinical signs when fentanyl was added. The EEG patterns typical for a specific clinical condition remained unchanged by fentanyl. Similar clinical situations showed equal EEG stages in all groups. Different clinical situations could be distinguished by significant changes in the EEG. The infusion times for tolerance to pain and respiratory insufficiency were not significantly different, and there were no significant differences between the EEG patterns and propofol doses for these two clinical parameters. Intubation was performed after 18.5 +/- 4.6 min in group I with a propofol dose of 5.6 +/- 1.4 mg/kg. This time was shortened by fentanyl in group II to 10.1 +/- 3.7 min and a propofol dose of 3.0 +/- 1.1 mg/kg. CONCLUSION: Different clinical signs corresponding to different levels of depth of anaesthesia could be differentiated by their EEG parameters. The EEG stage allowed better differentiation of the clinical conditions than the single-parameter EEG derivatives median and SEF. The results of this study show that EEG monitoring provides information about depth of anaesthesia.

Adjuvants, Anesthesia↗
PubMed2005

Do clinical variables predict an abnormal EEG in patients with complex febrile seizures?

PURPOSE: Febrile seizures are the commonest convulsive event in children younger than 5 years of age (incidence of 2-5%). Electroencephalography (EEG) is not indicated in the work up of simple febrile seizures. Information about its role in the assessment of complex febrile seizures (CFSs) is unclear and EEGs are frequently ordered. This study was designed to assess utility of clinical variables at presentation in predicting the likelihood of an abnormal EEG. METHODS: EEG requisitions, EEG reports, clinic charts and medical records over an 11-year period (1990--2001) were retrospectively reviewed. The relationship between clinical variables like age, timing of the EEG since CFS, family history of seizures, neurological assessment and EEG abnormalities was statistically analyzed. RESULTS: One hundred and seventy-five children were included in the study. Of these 39.43% had EEG abnormalities. Children with a normal EEG were younger than those with an abnormal EEG (mean age 15.72 months versus 19.75 months, p<0.05). Using multivariate analysis, factors predictive of abnormal EEGs in children with CFS were; age >3 years (p=0.010; 95% CI: 1.5--18.8), EEGs performed within 7 days (p=0.00; 95% CI: 1.78--7.12) and an abnormal neurological exam (p=0.053; 95% CI: 0.98--16.9). A family history of febrile seizures was more likely to be associated with a normal EEG (p=0.01; 95% CI: 0.04--0.60). CONCLUSIONS: Clinical variables at presentation can be used to screen children with CFS for whom an EEG is considered. This may lead to better use of resources. Whether abnormal EEG translates to future recurrences or epilepsy needs a prospective study.

Child, Preschool↗
PubMed2006

EEG departments in Great Britain: a survey of practice.

OBJECTIVE: Guidelines have been published recently on when an electroencephalogram (EEG) should be undertaken and the expected waiting time for a routine EEG, but have not included recommendations on how an EEG should be undertaken or who should undertake and report EEGs. The purpose of this survey was to obtain background information on the composition and practice of EEG departments throughout Great Britain that might form the basis for additional recommendations and guidelines. METHODS: A postal questionnaire was sent to all EEG departments in Great Britain in 2001/2002; non-responders were sent a further copy of the questionnaire. RESULTS: Fifty-two departments responded, representing a response rate of 48%. The reasons for this relatively poor response rate are not known. Findings indicated that a single doctor reported EEGs in 44% of all departments. The speciality of doctor reporting EEGs included neurophysiologists (59%), neurologists (22%), paediatric neurologists (PNs) (13%), psychiatrists (5%) and one general practitioner (GP). EEG recordists from 28 departments (54%) and medical staff in 21 departments (40%) stated that they had not received any formal training in, respectively, undertaking and reporting EEGs in children. In 29 departments (55%), medical staff report EEGs independently from the recordists. Fourteen departments (27%) undertake less than 500 EEGs per year. Ten of the 42 departments providing a paediatric service perform less than 100 paediatric EEGs per year. Eighteen (35%) and eight (15%) units have a waiting time of 2-4 and over 12 weeks, respectively, for a routine EEG. CONCLUSIONS: This survey has identified a range of practice and operational procedures in 52 EEG departments throughout Great Britain. These findings may have implications on the investigation and management of people with non-epileptic seizures and epilepsy.

Adult↗
PubMed2006

MEG versus EEG: influence of background activity on interictal spike detection.

The comparative sensitivity of EEG and magnetoencephalography (MEG) in the visual detection of focal epileptiform activity in simultaneous interictal sleep recordings were investigated. The authors examined 14 patients aged 3.5 to 17 years with localization-related epilepsy. Simultaneous 122-channel whole-head MEG and 33-channel EEG were recorded for 20 to 40 minutes during spontaneous sleep. The EEG and MEG data were separated and four blinded independent reviewers marked the presence and timing of epileptic discharges (ED) in the 28 data segments. EEG and MEG data were matched and spikes identified by at least three reviewers were classified in three categories according to the following criteria: type 1 MEG > EEG, type 2 EEG > MEG (type 1/2: difference of three or more raters), and type 3 EEG = MEG (three or more raters each). The presence of simultaneous sleep changes was visually determined for every single EEG-segment. Spikes with high spatiotemporal correlation were averaged and subjected to single dipole analysis of peak activity in EEG. Out of 4704 marked patterns, 1387 spikes fulfilled the above criteria. In fact, more spikes were unique to MEG (689) than to EEG (136) and to the combination of both modalities (562). ED were detected predominantly by MEG in eight patients and by EEG in two patients. The presence of vertex waves and spindles lead to a significantly higher number of spikes identified only in MEG. Averaging of type 1 spikes produced clear spike activity in EEG in 9 of 12 cases. On the contrary, only 2 of 10 type 2 spikes were visible in MEG after averaging. Dipoles of spikes visible in MEG showed a more tangential orientation compared with more radial dipoles of type 2 spikes. Spike characteristics, e.g., dipole orientation, are a key factor for a sole EEG representation. Exclusive MEG detection is more likely influenced by overlapping background activity in EEG. Because MEG is indifferent to radial activity, i.e., sleep changes, a higher ratio of spikes unique to MEG compared with EEG is detected in the case of overlapping sleep changes.

Action Potentials↗
PubMed1999

Correlated variations in EEG pattern and visual responsiveness of cat lateral geniculate relay cells.

1. Simultaneous recordings of the EEG and the visual activity of cat dorsal lateral geniculate nucleus (dLGN) relay cells were analysed for covariance. Sliding time-window analyses were performed in parallel for the EEG power spectrum and single unit visual activity. The EEG power ratio (EEG-PR) of low (1-8 Hz) to high (20-40 Hz) frequencies was chosen to achieve a quantitative measure of the EEG which could be compared with the spike rate of a dLGN unit at any time. A high EEG-PR value indicates a synchronized EEG dominated by low frequencies (delta waves and sleep spindles), a low value indicates a less synchronized EEG. 2. In the anaesthetized animal, two different underlying patterns of activity in the EEG-PR were found: slow gradual changes (slow gradations) and oscillatory changes. In many cases both were accompanied by correlated variations in dLGN spike rate, either for overall activity or for burst firing. 3. The slow gradations appear for long time periods of up to 200 s and, in most cases (76.3 %), show a negative correlation between EEG-PR and overall spike rate, but predominantly a positive correlation for burst firing (85.1 %). 4. The oscillatory changes, which have not previously been reported, appear as temporally well-coupled variations in EEG-PR and spike rate with a stable cycle length within the range 4-10 s. In about 77 % of correlated changes the temporal delay between the change in EEG-PR and that of the spike rate was less than +/- 1.0 s. 5. During simultaneous recordings from two dLGN cells the variations in spike rate tend to show the same sign of correlation with respect to the EEG pattern. This relationship is more pronounced with the slow gradations than with the oscillatory changes. 6. Slow gradations in the spectral composition of the EEG may indicate global transitions between different stages within the sleep-wake cycle, reflecting the well-known influences of the brainstem arousal system. The oscillations in the spectral composition of the EEG are accompanied by gradual variations in thalamic transmission mode and are more likely to be due to involvement of a local feedback system via the thalamo-cortico-thalamic loop. The difference between the effects on overall and burst firing activity supports the notion that phasic (burst firing) and tonic visual responses may play distinctive roles in information processing, which are functionally related to the animal's behavioural state.

Animals↗
PubMed2002

Postsynaptic muscarinic M1 receptors activate prefrontal cortical EEG of C57BL/6J mouse.

Recent pharmacological studies exploring the functional roles of muscarinic cholinergic receptor (mAChR) subtypes in prefrontal cortex of C57BL/6J (B6) mouse have provided evidence for a presynaptic M2 autoreceptor. The B6 mouse was chosen for these studies because it is a genetically well-characterized model that also provides the genomic background for many genetically modified mice. In addition to increasing ACh release, one functional consequence of pharmacologically blocking the cortical M2 autoreceptor is activation of the contralateral prefrontal cortical EEG. To date, the mechanisms through which M2 autoreceptor antagonism causes cortical EEG activation have not been investigated. The present study tested the hypothesis that, in the B6 mouse, prefrontal cortical ACh activates the contralateral prefrontal EEG via postsynaptic M1 receptors. This hypothesis was tested in 15 mice using in vivo microdialysis delivery of muscarinic antagonists with simultaneous quantification of ACh release, number of 7- to 14-Hz EEG spindles, and fast Fourier transformation analysis of prefrontal EEG. Dialysis delivery of the nonsubtype selective muscarinic antagonist scopolamine (10 nM) significantly (P = 0.01) increased ACh release. Quantitative EEG analysis showed that scopolamine did not alter contralateral prefrontal cortical EEG. To differentiate mAChR subtypes mediating pre- versus postsynaptic responses, additional experiments used muscarinic antagonists with different affinities for the five mAChR subtypes. Microdialysis delivery of 3 nM AF-DX 116, a muscarinic antagonist with relatively high affinity for the M2 and M4 subtypes, significantly (P < 0.01) increased prefrontal cortical ACh release and activated EEG in the contralateral prefrontal cortex. EEG activation was characterized by a significant decrease in number of 7- to 14-Hz EEG spindles (P < 0.0001) and power (Vrms) of EEG slow waves (P < 0.05). Microdialysis delivery of 3 nM AF-DX 116 plus 3 nM pirenzepine, a relatively selective M1 and M4 muscarinic antagonist, also significantly (P < 0.01) increased ACh release but did not decrease the number of EEG spindles and did not change EEG slow waves. The differential EEG and ACh responses to dialysis delivery of the muscarinic antagonists support the conclusion that, in B6 mouse, postsynaptic muscarinic receptors of the M1 subtype are a primary site by which ACh activates the EEG.

Animals↗
PubMed1997

CNS late effects after ALL therapy in childhood. Part II: Conventional EEG recordings in asymptomatic long-term survivors of childhood ALL--an evaluation of the interferences between neurophysiology, neurology, psychology, and CNS morphology. German Late Effects Working Group.

Monitoring of therapy-related late effects after acute lymphoblastic leukemia (ALL) therapy in childhood has become an increasingly important area in posttherapeutic patient surveillance. The usefulness of conventional electro-encephalographic (EEG) investigations as part of these attempts is controversially discussed. However, EEG recordings have become a popular approach for judgement on the functional integrity of the central nervous system in this subject group. The present report focuses on this problem and discusses the question whether and to what extent conventional EEG recordings were correlated with further measures of central nervous system (CNS) integrity and therapeutic differences. EEGs were recorded in 110 subjects, asymptomatic long-term survivors of ALL in childhood, during a large retrospective multicenter study evaluating CNS late sequelae following antileukemic therapy in Germany and Austria. EEG findings were correlated with demographic data, illness- and treatment-related parameters, as well as with data on the morphological, neurological and psychological status of the participating subjects. At the time of follow-up the EEG was abnormal in 47 cases (42.7%). The most frequent EEG abnormalities observed were disturbances of the background activity (n = 45, 95.8%), followed by hypersynchrone activities (n = 1.0, 21.3%) and interhemispheric differences/focal slowing (n = 6, 12.8%). With exception of age at diagnosis, none of the observed EEG abnormalities showed a correlation with any of the aforementioned illness- or treatment-related parameters. Eighty percent of the observed EEG abnormalities were found in children younger than 5 years at diagnosis. Children less than 2 years of age as well as those above 5 years at onset of disease showed a significantly reduced prevalence of EEG disturbances compared to subjects between 2 and 5 years at diagnosis. Neither the degree of illness nor therapy-specific differences showed any relationship to EEG outcome. There was no specific EEG finding for a specific morphological substrate, neurological or psychological deficiency and vice versa. Overall, there was no beneficial effect of routine EEG testing in children following therapy for ALL. According to our data, the evaluation of conventional EEG recordings of otherwise asymptomatic ALL long-term survivors is not a very helpful measure for predicting the degree of behavioral deficiencies, neurological disturbances, or morphological CNS abnormalities, which may be present or will develop in this special subject group.

Adolescent↗
PubMed2004

Multimodal integration of EEG and MEG data: a simulation study with variable signal-to-noise ratio and number of sensors.

Previous simulation studies have stressed the importance of the multimodal integration of electroencephalography (EEG) and magnetoencephalography (MEG) data in the estimation of cortical current density. In such studies, no systematic variations of the signal-to-noise ratio (SNR) and of the number of sensors were explicitly taken into account in the estimation process. We investigated effects of variable SNR and number of sensors on the accuracy of current density estimate by using multimodal EEG and MEG data. This was done by using as the dependent variable both the correlation coefficient (CC) and the relative error (RE) between imposed and estimated waveforms at the level of cortical region of interests (ROI). A realistic head and cortical surface model was used. Factors used in the simulations were: (1). the SNR of the simulated scalp data (with seven levels: infinite, 30, 20, 10, 5, 3, 1); (2). the particular inverse operator used to estimate the cortical source activity from the simulated scalp data (INVERSE, with two levels, including minimum norm and weighted minimum norm); and (3). the number of EEG or MEG sensors employed in the analysis (SENSORS, with three levels: 128, 61, 29 for EEG and 153, 61, or 38 in MEG). Analysis of variance demonstrated that all the considered factors significantly affect the CC and the RE indexes. Combined EEG-MEG data produced statistically significant lower RE and higher CC in source current density reconstructions compared to that estimated by the EEG and MEG data considered separately. These observations hold for the range of SNR values presented by the analyzed data. The superiority of current density estimation by multimodal integration of EEG and MEG was not due to differences in number of sensors between unimodal (EEG, MEG) and combined (EEG-MEG) inverse estimates. In fact, the current density estimate relative to the EEG-MEG multimodal integration involved 61 EEG plus 63 MEG sensors, whereas estimations carried out with the single modalities alone involved 128 sensors for EEG and 153 sensors for MEG. The results of the simulations also suggest that the use of simultaneous 29 EEG sensors during the MEG measurements carried out with full sensor arrangements (153 sensors) returned an accuracy of the cortical source estimate statistically similar to that obtained by combining 64 EEG and 153 MEG sensors.

Brain↗
PubMed2002

Interictal EEG abnormalities in patients with psychogenic nonepileptic seizures.

PURPOSE: To examine interictal EEG abnormalities in patients with psychogenic nonepileptic seizures (PNESs). METHODS: (a) Retrospective study of EEG reports of 187 consecutive patients with PNES seen at the Department of Epileptology, Bonn, Germany; (b) Blinded, multirater comparison of EEGs of all PNES patients with no other clinically recognizable cause of EEG disturbance (n = 50) and healthy controls (n = 50). RESULTS: Of 187 consecutive patients with PNESs, 57 patients had PNESs and epilepsy (PNES+E), and 130 patients, PNESs alone. The diagnosis of additional epilepsy was based on ictal (video-) EEG or on the critical assessment of all clinical data by an experienced epileptologist. Retrospective review of all available EEG reports showed that 92.9% of patients in the PNES+E and 53.8% in the PNES-only group had one or more abnormal EEGs (median number of EEGs per patient, three; range, one to 42). In the PNES-only group, EEG changes were nonspecific in 42.3% of patients. Only 50 of 130 patients with PNESs alone had no other clinically recognizable cause of EEG disturbance and entered the controlled study. In this study, 18% of patients and 10% of controls had abnormal EEGs. The frequency of epileptiform EEG changes was similar to that in previous population studies in both groups (2.0%). CONCLUSIONS: PNESs often occur in patients with organic brain disease. Even in patients with PNESs alone and no clinically recognizable cause of EEG disturbance, nonspecific abnormalities are found 1.8 times as often as in healthy controls. Interictal EEG changes are common in patients with PNESs and, in isolation, should not be interpreted as evidence of epilepsy.

Adolescent↗
PubMed2003

The influence of sulthiame on EEG in children with benign childhood epilepsy with centrotemporal spikes (BECTS).

PURPOSE: To evaluate the effects of sulthiame (Ospolot; STM) monotherapy compared with placebo on the EEG in children with benign childhood epilepsy with centrotemporal spikes (BECTS). METHODS: Sixty-six patients (aged 3-11 years) entered a 6-month double-blind trial and were randomized to either STM (n = 31) or placebo (n = 35). Clinical data and general results have been reported elsewhere (1). One-hundred seventy-nine sleep EEGs were recorded at screening and after 4 weeks, 3 months, and 6 months. EEGs were analyzed by a blind reviewer using a standard protocol for each EEG. This standard protocol collected data on general changes, specific epileptiform, and nonspecific focal and generalized changes. A classification system was defined depending on rating of pathologic EEG changes. Because of the higher number of treatment-failure events (i.e., seizures) in the placebo group, there was an increasing imbalance between the two groups regarding the number of recorded sleep EEGs over time (STM, 104; placebo, 74). A Wilcoxon-Mann-Whitney U test was used to describe differences in the grade of pathology during individual follow-up between the two groups. RESULTS: The sleep-EEG was found to be normalized in 21 patients treated with STM (12/21 transient) and in five patients treated with placebo (4/5 transient). In the STM group, the EEG showed a marked improvement during intraindividual course when comparing the classification of follow-up EEGs at each time point with the screening EEG. Comparable improvements were not observed in the placebo group (exact two-tailed p value at 4 weeks, p < 0.0001; at 3 months, p = 0.0010; and at 6 months, p < 0.0001). CONCLUSIONS: STM had marked effects on the EEG in BECTS, which led to normalization in the majority of the patients. Most of those whose EEGs were not normalized showed improvement in the grade of EEG pathology. Normalization persisted in >50% of patients during the investigation. Spontaneous normalization in the placebo group reflects the wide spectrum of individual courses, which must be considered when analyzing drug effects on EEG in BECTS.

Anticonvulsants↗
PubMed1983

The diagnostic value of the sleep EEG with and without sleep deprivation in patients with atypical absences.

Hitherto it has not been known whether or not the sleep EEG after sleep deprivation is more effective than the simple or drug-induced sleep EEG. To investigate this, we recorded for 32 patients both sleep EEGs without sleep deprivation and, on the following day, sleep EEGs after 24 h of sleep deprivation. All the patients had atypical absences which were almost exclusively combined with generalized seizures or some other seizure types. All patients were receiving antiepileptic therapy. Sleep without sleep deprivation was induced by oral administration of 2 mg/kg body weight Protactyl (promazine hydrochloride). In patients showing no epileptic activity in the routine EEG, epileptic discharges could be provoked in 78% without sleep deprivation and in 72% after sleep deprivation. Epileptic activity was already seen in 28.1% of the cases in the awake EEG without sleep deprivation, recorded immediately before the sleep EEG, and in 50% of the cases in the awake EEG after sleep deprivation. It is unlikely that promazine hydrochloride in the dose used here has an additional inherent provocative effect. Generalized spike-and-wave complexes or sharp slow wave complexes which were combined twice with foci and runs of rapid spikes were recorded. In the sleep EEG without sleep deprivation, epileptic discharges were seen in the somewhat shallower stages (C leads to A leads to B leads to D) and, in the sleep EEG after sleep deprivation, in the somewhat deeper stages (D leads to C leads to B leads to A). Fewer epileptic discharges were elicited in patients who were older at the time of their first seizure. The illness was mild in patients whose sleep EEGs showed no epileptic activity. It is concluded that, as a rule, it is not necessary to record an EEG after sleep deprivation in patients with atypical absences whose routine EEGs show no epileptic activity; the drug-induced sleep EEG shows the same provocative effect.

Adolescent↗
PubMed1993

A quantitative method for classification of EEG in the fetal baboon.

Electroencephalographic (EEG) activity is used as a primary indicator of sleep states in adults and infants of many species and in the ovine fetus. We recently reported that the baboon fetus exhibits visually discernable patterns of EEG activity. One pattern of activity, characterized by the intermittent presence of repetitive bursts of high-voltage EEG, is indistinguishable from trace alternant (TA). TA is a distinctive pattern of EEG activity found only during early stages of development in primates. TA is the predominant pattern of EEG activity during quiet sleep in human infants < 2 mo of age. The focus of this study was to derive quantitative parameters that would discriminate TA from other activity and then to develop a method for automated categorization of EEG patterns. Results demonstrate that several parameters derived from frequency-domain analyses are related to visually coded EEG states. Among these parameters, high-frequency power (12-24 Hz) and spectral-edge frequency are good discriminators of EEG patterns. This paper describes a new parameter, EEG ratio, computed as spectral power in the rectified EEG within a band that corresponds to the frequency of bursts of activity during TA (0.03-0.20 Hz) divided by power in the 12- to 24-Hz band. This new composite parameter of EEG activity provides a markedly better correlate of visually coded EEG than any of the individual parameters tested. Using cluster analysis, we devised a method for objective minute-by-minute dichotomization of EEG ratio. The method produces results that agree with visual coding of EEG activity 87.1% of the time.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗
PubMed2005

[EEG recording after sleep deprivation in a series of patients with juvenile myoclonic epilepsy].

Seizures in Juvenile Myoclonic Epilepsy (JME) are dependent on the sleep-wake cycle and precipitant factors, among which sleep deprivation (SD) is one of the most important. Still an under diagnosed syndrome, misinterpretation of the EEGs contributes to diagnostic delay. Despite this, a quantitative EEG investigation of SD effects has not been performed. We investigated the effect of SD on EEGs in 41 patients, aged 16-50 yr. (mean 25.4), who had not yet had syndromic diagnosis after a mean delay of 8.2 yr. Two EEG recordings separated by a 48-hour interval were taken at 7 a.m. preceded by a period of 6 hours of sleep (routine EEG) and after SD (sleep-deprived EEG). The same protocol was followed and included a rest wakefulness recording, photic stimulation, hyperventilation and a post-hyperventilation period. The EEGs were analyzed as to the effect of SD on the number, duration, morphology, localization and predominance of abnormalities in the different stages. A discharge index (DI) was calculated. Out of the 41 patients, 4 presented both normal EEG recordings. In 37 (90.2%) there were epileptiform discharges (ED). The number of patients with ED ascended from 26 (70.3%) in the routine EEG to 32 (86.5%) in the sleep-deprived exam. The presence of generalized spike-wave and multispike-wave increased from 20 (54.1%) and 13 (35.1%) in the first EEG to 29 (78.4%) and 19 (51.4%) in the second, respectively (p<0.05 and p<0.01). As to localization, the number of generalized, bilateral and synchronous ED increased from 21 (56.8%) to 30 (81.1%) (p<0.01). The DI also increased; while 8 patients (21.6%) presented greater rate in the routine EEG, 25 (67.6%) did so in the sleep-deprived EEG mainly during somnolence and sleep (p<0.01). Moreover, the paroxysms were also longer in the sleep-deprived EEG. Sleep-deprived EEG is a powerful tool in JME and can contribute significantly to the syndromic characterization of this syndrome.

Adolescent↗