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PubMed1996

Routine EEG and temporal lobe epilepsy: relation to long-term EEG monitoring, quantitative MRI, and operative outcome.

PURPOSE: To investigate the relation among routine EEG, long-term EEG monitoring (LTM), quantitative magnetic resonance imaging (MRI), and surgical outcome in temporal lobe epilepsy (TLE). METHODS: We evaluated 159 patients with intractable TLE who underwent an anterior temporal lobectomy between 1988 and 1993. The epileptogenic temporal lobe was determined by ictal LTM. A single awake-sleep outpatient EEG with standard activating procedures was performed before LTM. EEGs were analyzed by a blinded investigator. RESULTS: MRI scans showed unilateral medial temporal atrophy (109 patients) or symmetrical hippocampal volumes (50 patients). The surgically excised epileptogenic brain tissue revealed mesial temporal sclerosis, gliosis, or no histopathologic alteration. Routine EEG revealed temporal lobe epileptiform discharges in 123 patients. Routine EEG findings correlated with the temporal lobe of seizure origin (p < 0.0001) and the results of MRI volumetric studies (p < 0.0001). Interictal epileptiform discharges were seen only during LTM in 24 patients. Routine EEG was disconcordant with interictal LTM in another 20 patients. MRI-identified unilateral medial temporal lobe atrophy was a strong predictor of operative success (p < 0.0001). There was no significant relation between the routine EEG findings and operative outcome (p > 0.20). CONCLUSIONS: Results of this study modified our approach in patients with TLE. Interictal epileptiform discharges localized to one temporal lobe on serial routine EEGs or during LTM may be adequate to identify the epileptogenic zone in patients with MRI-identified unilateral medial temporal lobe atrophy.

Adolescent↗

PubMed1995

Prognostic value of electroencephalogram (EEG) in anoxic encephalopathy after cardiopulmonary resuscitation: relationship among anoxic period, EEG grading and outcome.

We studied the prognostic applicability of electroencephalograms (EEGs) of seventy-nine patients within 24 hours after successful cardiopulmonary resuscitation. The EEGs were classified into five grades according to a modified Hockaday's scale. The EEGs of grades I and II implied full recovery, while those of grade III gave a varied but generally unfavorable prognosis. Patients with grades IV and V EEGs survived in a vegetative condition or died without awakening. Eighteen patients showed EEG with periodic patterns, all of which led to a fatal or vegetative outcome. One case showed EEGs associated with periodic triphasic waves and repetitive sharp transients in the same record. Several cases showed EEGs with different periodic patterns in consecutive records. We conclude that an EEG is a good indicator of patient prognosis after cardiopulmonary resuscitation. However, the clinical significance of morphological differences of various periodic patterns that can occur during an EEG remains to be established.

Adult↗

PubMed1979

The electroencephalogram (EEG) as a research tool in human behavior genetics: psychological examinations in healthy males with various inherited EEG variants. I. Rationale of the study. Material. Methods. Heritability of test parameters.

In the first section of this paper, various research designs in human behavior genetics are compared. In this context, the commonly used concept of biometric genetics is critically evaluated from the point of view of science theory. It is contrasted with the Mendelian gene concept, which, in principle, leads to a much deeper theoretical understanding by offering clues for basic mechanisms. To explore this advantage fully, a research strategy is needed that first looks for genetic variability in a physiological parameter of possible importance for human behavior and then tries to explore the influence of this parameter on the function of the human brain and on behavior. If possible, this genetic parameter should be selected in a way that inferences as to the mechanism of its influence on behavior become feasible. Such genetic variability is provided by the hereditary variants of the normal EEG discovered by earlier work (cf. Vogel, 1970). In the following section, a research program on 298 adult healthy males, most of them soldiers, with various inherited EEG variants is described. Apart from controls with inconspicuous EEGs, this material comprises probands with the following EEG variants: low-voltage (N); low-voltage borderline (NG); monotonous alpha-waves (R); occipital fast alpha-variants (BO); fronto-precentral beta-groups (BG), and diffuse beta-waves (BD). In addition to an EEG examination, the probands were examined with various test methods measuring intelligence (IST; LPS; Raven); working speed and concentration (d-2; KLT); personal attitudes (MMPI; 16PF; RKS); and sensory and motor abilities (flicker fusion; tachistoscopy; reaction time to optic, acoustic and combined stimuli; two-hand dexterity; pursuit rotor; tapping). In a supplementary twin study on 52 male adult twin pairs (26 MZ, 26 DZ), heritabilities were determined for the test scores included in the main study. For most test scores, heritabilities are relatively low; the data are compared with those from the literature. We conclude that the test methods utilized in the main study (on EEG variants) are expected to demonstrate at the most a small to moderate correlation of the EEGs with psychological phenotypes as defined by test examinations, even if a major part of the genetic variability underlying these phenotypes would be due to differences in brain physiology that could be revealed by EEG variation.

Adult↗

PubMed1992

A comparison of quantitative EEG frequency analysis and conventional EEG in patients with focal brain lesions.

The sensitivity and specificity of quantified EEG frequency analysis (EEGFA) were assessed in a group of patients with CT or MRI verified unilateral cerebral lesions and compared with the results of conventional EEG interpretations. Digital EEG recordings were obtained in 25 patients and 25 normal control subjects during performance of an alerting task. Recording artifacts were carefully eliminated. The results of EEGFA were then statistically compared with those of 75 additional normal subjects in the same age range. Complete conventional EEGs were blindly interpreted by two independent electroencephalographers using a structured reporting procedure. We observed similar overall sensitivities for the two methods. Optimal yield for EEGFA was associated with the use of longer edited EEG lengths, longitudinal bipolar montage, and normative data based on total EEG power. In those recordings with normal or mildly abnormal EEG backgrounds the two techniques were to an extent complimentary, each detecting abnormalities missed by the other. In such circumstances EEGFA may be useful as an extension of conventional EEG interpretation.

Adult↗

PubMed1994

Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data.

We have developed and tested "off-line" an artificial neural network (ANN) that successfully detects epileptiform discharges (EDs) when trained on EEG records marked by an electroencephalographer (EEGer). The system was trained on both parameterized and raw EEG data and can process 49 channels of EEG data in real time on an 80486/33 MHz personal computer, making it capable of processing EEG on-line in long-term monitoring units. Our detector consists of 2 stages: (1) a threshold detector identifies candidate EDs in 4-channel bipolar chains within the recording montage, parameterizes them and then passes these data to the second stage; (2) a 3-layer feed-forward ANN decides if a candidate wave form is an ED. The intersection of detector sensitivity and selectivity curves, or crossover threshold, for 10 patients from our Epilepsy Monitoring Unit occurred at 73% for parameterized EEG data and at 46% for "raw" EEG data. The ANN could be adapted to different EEGers' styles by changing the ANN output threshold for accepting candidate wave forms as EDs. In this "proof of principle" study the detector was trained on EEGs from 10 Johns Hopkins Hospital Epilepsy Monitoring Unit (JHH EMU) patients. We used different EEGs from the same patients for testing. Current testing should demonstrate that the ANN detector can generalize to previously "unseen" patients. This study shows that ANNs offer a practical solution for automated, real time ED detection that uses, standard, inexpensive computers, is easily adjustable to individual EEGer style and can produce sensitivities and selectivities similar to those of EEGers.

Brain↗

PubMedDate not supplied

Mu-delta opioid interactions. I: The delta peptide, DPDPE, increases morphine-induced EEG and EEG spectral power.

The effects of the selective delta peptide, DPDPE ([2-D-penicillamine,5-D-penicillamine]enkephalin), on morphine-induced EEG and EEG power spectra were assessed. Adult female Sprague-Dawley rats were implanted with cortical EEG electrodes and permanent indwelling ICV and IV cannulae. Rats were pretreated with ICV sterile water or ICV DPDPE at 1.25, 2.5, or 5.0 nmol, 10 min before receiving IV morphine at 3 mg/kg. The treatments produced high voltage EEG bursts and associated behavioral stupor. The EEG data were further analyzed on a Pathfinder II computer and statistical analysis of EEG spectral parameters was performed by using a one-way ANOVA followed by Turkey's HSD. ICV DPDPE at 2.5 nmol and 5.0 nmol significantly increased EEG absolute global spectral power. Furthermore, ICV DPDPE at 2.5 and 5.0 nmol significantly increased the duration of morphine-induced high voltage EEG bursts. These data further indicate an in vivo interaction between mu and delta opioid receptors.

Analgesics↗

PubMed2005

Interictal EEG as a physiological adaptation. Part I. Composition of brain oscillations in interictal EEG.

OBJECTIVE: In the present experimental study, we examined the compositions of brain oscillations and their temporal behavior in broad frequency band (0.5-30 Hz) in interictal EEG without epileptiform abnormalities during generalized epilepsy in resting conditions. METHODS: The exact compositions of brain oscillations and their percent ratio were assessed by a probability-classification analysis of short-term EEG spectral patterns (SPs), which reveals temporal dynamics of these SPs and results in the probability-classification profile. RESULTS: It has been demonstrated that the interictal EEG was characterized by (a) a shift towards higher frequencies in all observed brain oscillations, (b) an increased amount of polyrhythmic activity, (c) a decrease in SP types diversity, (d) a decreased relative incidence of the SP type change in the transition between neighboring EEG epochs of the same EEG, and (e) an increased temporal stabilization periods of polyrhythmic activity. All these were observed in distributed brain areas. CONCLUSIONS: It was suggested that these findings reflect a disorganization of neurodynamics in the epileptic brain. At the same time, the fact that all these indices were significantly different from surrogate EEG reflects a non-occasional and thus, most likely, an adaptive nature of the microstructural reorganization of interictal EEG. SIGNIFICANCE: Parameters of interictal EEG without the signs of epileptiform activity can be considered as additional information in premorbid diagnostics of epistatus.

Adaptation, Physiological↗

PubMed2006

Increase in high frequency EEG activity explains the poor performance of EEG spectral entropy monitor during S-ketamine anesthesia.

OBJECTIVE: To study the effects of S-ketamine on the EEG and to investigate whether spectral entropy of the EEG can be used to assess the depth of hypnosis during S-ketamine anesthesia. METHODS: The effects of sub-anesthetic (159 (21); mean (SD) ng/ml) and anesthetic (1,959 (442) ng/ml) serum concentrations of S-ketamine on state entropy (SE), response entropy (RE) and classical EEG spectral power variables (recorded using the Entropy Module, GE Healthcare, Helsinki, Finland) were studied in 8 healthy males. These EEG data were compared with EEG recordings from 6 matching subjects anesthetized with propofol. RESULTS: The entropy values decreased from the baseline SE 85 (3) and RE 96 (3) to SE 55 (18) and RE 72 (17) during S-ketamine anesthesia but both inter- and intra-individual variation of entropy indices was wide and their specificity to indicate unconsciousness was poor. Propofol induced more pronounced increase in delta power (P<0.02) than S-ketamine, whereas anesthetic S-ketamine induced more high frequency EEG activity in the gamma band (P<0.001). Relative power of 20-70 Hz EEG activity was associated with high SE (P=0.02) and RE (P=0.03) values during S-ketamine anesthesia. CONCLUSIONS: These differences in low and high frequency EEG power bands probably explain why entropy monitor, while adequate for propofol, is not suitable for assessing the depth of S-ketamine anesthesia. SIGNIFICANCE: The entropy monitor is not adequate for monitoring S-ketamine-induced hypnosis.

Adult↗

PubMed2006

EEG-fMRI mapping of asymmetrical delta activity in a patient with refractory epilepsy is concordant with the epileptogenic region determined by intracranial EEG.

We studied a patient with refractory focal epilepsy using continuous EEG-correlated fMRI. Seizures were characterized by head turning to the left and clonic jerking of the left arm, suggesting a right frontal epileptogenic region. Interictal EEG showed occasional runs of independent nonlateralized slow activity in the delta band with right frontocentral dominance and had no lateralizing value. Ictal scalp EEG had no lateralizing value. Ictal scalp EEG suggested right-sided central slow activity preceding some seizures. Structural 3-T MRI showed no abnormality. There was no clear epileptiform abnormality during simultaneous EEG-fMRI. We therefore modeled asymmetrical EEG delta activity at 1-3 Hz near frontocentral electrode positions. Significant blood oxygen level-dependent (BOLD) signal changes in the right superior frontal gyrus correlated with right frontal oscillations at 1-3 Hz but not at 4-7 Hz and with neither of the two frequency bands when derived from contralateral or posterior electrode positions, which served as controls. Motor fMRI activations with a finger-tapping paradigm were asymmetrical: they were more anterior for the left hand compared with the right and were near the aforementioned EEG-correlated signal changes. A right frontocentral perirolandic seizure onset was identified with a subdural grid recording, and electric stimulation of the adjacent contact produced motor responses in the left arm and after discharges. The fMRI localization of the left hand motor and the detected BOLD activation associated with modeled slow activity suggest a role for localization of the epileptogenic region with EEG-fMRI even in the absence of clear interictal discharges.

Brain Mapping↗

PubMed1997

Disorganized patterns: chronic-stage EEG abnormality of the late neonatal period following severely depressed EEG activities in early preterm infants.

EEG recordings were performed within 72 hours following the birth of 55 preterm infants with a gestational age of less than 29 weeks. Seventeen infants (31%) manifested moderately to severely depressed EEG activities during this period. Eight of the infants died within a few days after birth, and 9 survived. We recorded EEGs from the surviving infants serially throughout the neonatal period, and evaluated EEG findings in the recovery phase following depressed EEG activities. At 1 to 3 weeks after birth, EEGs revealed an abnormal morphology of background activities in 8 of 9 cases; this was also true of the EEGs of 6 cases at 4 weeks or more of postnatal age. The latter 6 cases demonstrated deep white matter injury on cranial ultrasonography, and all of them later developed cerebral palsy. Based on the foregoing, EEG findings of early preterm infants in the late neonatal period are considered a useful means to detect deep white-matter injury and to allow neurological prognosis.

Brain Damage, Chronic↗

PubMed1984

Intraoperative EEG patterns: implications for EEG monitoring.

Univariate descriptors such as the mean frequency and spectral edge frequency have been proposed for intraoperative representation of the EEG. Such univariate descriptors of the EEG are accurate only when the EEG behaves as a unimodal distribution of frequencies that change slowly with time. EEGs were recorded from 64 patients undergoing anesthetic inductions and 30 patients undergoing cardiopulmonary bypass to determine the characteristics of the observed distribution of frequencies. Multimodal EEG activity was observed in 64% of these cases, including 83% of those patients undergoing cardiopulmonary bypass. The differences between the two peaks averaged 7.6 Hz, and the average ratio of the power of the peaks to the intervening valley was 2.5:1 and 1.9:1. Calculations of mean frequency and spectral edge frequency failed to adequately reflect the complexity of the EEG in these cases. Burst-suppression activity was observed in 26% of cases during cardiopulmonary bypass, and averaging over time destroyed the characteristic pattern. Thus, univariate descriptors of the EEG appear inadequate to describe the behavior of EEG during anesthesia in a large percentage of cases.

Alpha Rhythm↗

PubMed2005

Intracranial EEG substrates of scalp EEG interictal spikes.

PURPOSE: To determine the area of cortical generators of scalp EEG interictal spikes, such as those in the temporal lobe epilepsy. METHODS: We recorded simultaneously 26 channels of scalp EEG with subtemporal supplementary electrodes and 46 to 98 channels of intracranial EEG in 16 surgery candidates with temporal lobe epilepsy. Cerebral discharges with and without scalp EEG correlates were identified, and the area of cortical sources was estimated from the number of electrode contacts demonstrating concurrent depolarization. RESULTS: We reviewed approximately 600 interictal spikes recorded with intracranial EEG. Only a very few of these cortical spikes were associated with scalp recognizable potentials; 90% of cortical spikes with a source area of >10 cm(2) produced scalp EEG spikes, whereas only 10% of cortical spikes having <10 cm(2) of source area produced scalp potentials. Intracranial spikes with <6 cm(2) of area were never associated with scalp EEG spikes. CONCLUSIONS: Cerebral sources of scalp EEG spikes are larger than commonly thought. Synchronous or at least temporally overlapping activation of 10-20 cm(2) of gyral cortex is common. The attenuating property of the skull may actually serve a useful role in filtering out all but the most significant interictal discharges that can recruit substantial surrounding cortex.

Brain Mapping↗

PubMed2000

Automatic detection of open and closed eye states in the electroencephalographic (EEG) record for background EEG interpretation by the trigger method.

Automatic detection of open and closed eye states in electroencephalographic (EEG) records was investigated in this study as a part of procedures for the precise interpretation of the background EEG to achieve a comprehensive automatic EEG interpretation system. The features of eye open and closure were extracted from the EEG using a Markov process amplitude (MPA) EEG model, which could efficiently express the features of the EEG using a small number of parameters. A new technique, named the trigger method, was also developed to overcome the difficulty resulting from large differences in the EEG features among different subjects, because an accurate detection could not be obtained by using the conventional threshold method. The proposed method gave satisfactory results which conformed with those of visual inspection by a qualified EEGer and could be clinically used as a preprocessing method for the automatic interpretation of the awake background EEG.

Adult↗

PubMedDate not supplied

Automatic classification algorithms of the EEG monitor Narcotrend for routinely recorded EEG data from general anaesthesia: a validation study.

Impacts of hypnotic drugs on brain function are reflected in the EEG. The EEG monitor Narcotrend performs an automatic classification of the EEG using a scale which was proposed by Kugler for visual evaluation of the EEG. In this article the results of a validation study of the automatic classification algorithms implemented in the EEG monitor Narcotrend are presented. Visual and automatic classification of EEG data recorded in routine clinical practice were compared. The correlation between visual and automatic assessment was high (Spearman rank correlation r = 0.90, prediction probability Pk = 0.90) and a sufficient agreement between visual and automatic assessment was achieved for 92% of the analysed EEG epochs. The results of the study suggest that the automatic classification algorithms implemented in the EEG monitor Narcotrend yield a reliable assessment of the depth of hypnosis.

Algorithms↗

PubMed1992

[Automatic (computer-assisted) EEG analysis in comparison with visual EEG analysis in patients following minor cranio-cerebral trauma (a follow-up study)].

The study was designed to examine the clinical applicability and use of computer-assisted EEG-analysis in comparison to visual EEG-analysis in patients with minor head injuries. For this in 31 patients the following examinations were performed within the first 24 hours, one, three and eight weeks after traumatization: EEG, neurological examination and standardized evaluation of subjective complaints. The EEG was analysed visually as well as computer-assisted. The results were compared to the neurological findings and subjective complaints. Reversible posttraumatic EEG-changes were observed in 50% of all patients. The results of computer-analysis and visual analysis were equivalent in respect to baseline-activity (as one of the main characteristic EEG-features). Furthermore, based on computer-assisted analysis a discriminant function is provided, which is of diagnostic and prognostic value in the single patient. We conclude that the computer-assisted EEG-analysis can be a useful alternative in EEG-routine diagnosis after minor head injuries.

Adolescent↗

PubMed1993

[The EEG background activity in children with epilepsy--effects of the removal of anticonvulsants on the EEG].

The development of the EEG background activity is generally impaired in children with epilepsy. Antiepileptic drugs (AEDs) are one of the important factor which influence the EEG background activity. We studied the effect of AEDs on the EEG. Subjects were 46 normal children and 48 children with epilepsy aged 5 to 15, who had not any organic brain damage and showed normal psychomotor development. Forty children with epilepsy (CBZ; 30 cases, VPA; 10 cases) had been on monopharmacy and 8 cases on polypharmacy. All of them have outgrown their epilepsy. The serial EEGs were recorded during withdrawal and after discontinuation of AED in each patient. These records were evaluated using computerized power spectral analysis. CBZ reduction associated with a decrease in delta and theta powers and an increase in alpha-2 power, which were also observed at VPA reduction in concomitant AEDs (VPA+CBZ). No parameter changed by VPA reduction in monopharmacy. The slowing of the EEG background activity was noticed even after discontinuation of CBZ. It required more time after discontinuation of CBZ in younger children than older before the EEG background activity became normal controls. These findings suggested that CBZ had a adverse effect, which was more remarkable in younger children than older children, on the development of EEG background activity and that the effect of CBZ on EEG was different from that of VPA.

Adolescent↗

PubMed1996

Reliable computer-assisted classification of the EEG: EEG variants in index cases and their first degree relatives.

A method which optimizes on global properties of sample recordings is proposed for the definition of and the discrimination between electroencephalogram (EEG) classes. The sample was drawn from students at the University of Heidelberg from 1974 to 1978 and consists of 15 healthy index cases clinically ascertained as belonging to the low voltage EEG group. In addition, the three clinically defined groups: diffuse beta (18 index cases), borderline alpha (12 index cases) and monomorphous alpha (18 index cases) have been included in the study, as well as the first degree relatives of the index cases, thus providing a clinical classification into four groups. The proposed method provides an automatic and reliable classification algorithm using discriminant and cluster analysis. The relation between such an automatized classification and clinical classification schemes is investigated. In particular, the inheritance of the low voltage EEG, the question on sex differences and the question of a simple Mendelian mechanism had been examined. The method of random splittings had been applied for discriminant and cluster analysis. Our findings can be summarized as follows: (1) except for the monomorphous alpha EEG group, the clinical classification shows rather marginal separation (discriminating performance 60% to 75%), while a new and more reliable grouping scheme improves the discriminating performance up to 87% to 91%. The latter scheme leads to the concept of personal channel pattern (PCP) and was compared to the clinical classification scheme by means of contingency tables; (2) only a weak correlation between the clinically and PCP-based groups could be found (Cramér Index: 0.27). Accordingly, we continued to investigate the extent to which the proposed EEG classification scheme can nevertheless explain the genetic mechanisms apparently involved in the low voltage EEG. We thus considered the role of sex differences manifest in our proposed new grouping scheme; (3) males occurred more frequently in the new group 3 and females more frequently in the new group 1. In this regard, a much better correlation of the new groups between mothers and children than between fathers and children was observed; and (4) with help of our new PCP scheme, we have been able to reproduce a simple two gene Mendelian scheme to explain inheritance of the clinical low voltage EEG group. In this PCP-based scheme, the low voltage property does not occur when dominance of a certain gene (called gene A) is absent.

Adult↗

PubMed1988

Phase space electroencephalography (EEG): a new mode of intraoperative EEG analysis.

Intraoperative monitoring of electroencephalography (EEG) data can help assess brain integrity and/or depth of anesthesia. We demonstrate a computer generated technique which provides a visually robust display of EEG data plotted as 'phase space trajectories' and a mathematically derived parameter ('dimensionality') which may correlate with depth of anesthesia. Application of nonlinear mathematical analysis, used to describe complex dynamical systems, can characterize 'phase space' EEG patterns by identifying attractors (geometrical patterns in phase space corresponding to specific ordered EEG data subjects) and by quantifying the degree of order and chaos (calculation of dimensionality). Dimensionality calculations describe the degree of complexity in a signal and may generate a clinically useful univariate EEG descriptor of anesthetic depth. In this paper we describe and demonstrate phase space trajectories generated for sine waves, mixtures of sine waves, and white noise (random chaotic events). We also present EEG phase space trajectories and dimensionality calculations from a patient undergoing surgery and general anesthesia in 3 recognizable states: awake, anesthetized, and burst suppression. Phase space trajectories of the three states are visually distinguishable, and dimensionality calculations indicate that EEG progresses from 'chaos' (awake) to progressively more 'ordered' attractors (anesthetized and burst suppression).

Electroencephalography↗