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Biomedical subjects

G Dumermuth

Publications and source records attributed to G Dumermuth.

At least 19 recordsLinked to original sources
PubMed1993

Resuscitation after severe hypoxia in a young child: temporary isoelectric EEG and loss of BAEP components.

EEG and multimodality evoked potentials are currently used to ascertain brain death. We have observed electrocerebral silence (ECS) and loss of wave IV and V in BAEP in a 28-month-old child, 2 h after resuscitation from severe hypoxia, followed by reappearance of EEG activity and of waves IV and V (latencies and amplitudes within normal range) 18 h later. Temporary loss of EEG activity has been observed occasionally, while rapid reoccurrence of BAEP waves IV and V was unexpected and in contrast to the common observation, that rostro-caudal loss of BAEP waves is irreversible. This observation suggests that temporary loss of BAEP waves IV and V may occur and that caution is necessary in the interpretation of BAEP recorded immediately after a resuscitation.

Brain Death↗
PubMed1993

Nonketotic hyperglycinemia: clinical and electrophysiologic effects of dextromethorphan, an antagonist of the NMDA receptor.

A 10-week-old girl with nonketotic hyperglycinemia was treated with increasing amounts of dextromethorphan, an NMDA receptor antagonist. She improved neurologically; at 35 mg/kg/d, seizures ceased and EEG normalized. Dextromethorphan withdrawal resulted in a dramatic clinical deterioration coinciding with epileptic and high-voltage slow activity in the EEG. After reintroduction of dextromethorphan (35 mg/kg/d), recovery occurred within 24 hours.

Amino Acid Metabolism, Inborn Errors↗
PubMed1987

Differential diagnosis of syndromes with abnormal respiration (tachypnea-apnea).

Abnormal respiration with episodic tachypnea-apnea can occur in several syndromes (particularly, the Rett, Joubert, Mohr and Dandy-Walker syndromes). These syndromes are briefly reviewed. In the Rett syndrome, in contrast to in most other genetically determined syndromes, the onset of breathing abnormalities does not occur in the neonatal period.

Abnormalities, Multiple↗
PubMed1987

Spectral analysis of the EEG. Some fundamentals revisited and some open problems.

This tutorial was presented during the 1986 training course of the International Pharmaco-EEG Group (IPEG) in Santa Margherita Ligure, Italy. During recent years spectral analysis has been increasingly used in experimental EEG. However, to avoid misinterpretations of results, its limitations must still be carefully considered. The tutorial starts with revisiting the fundamentals of the technique, emphasizes the practical estimation of auto- and cross-spectra, discusses the assumptions underlying the spectral analysis of stochastic processes, and ends with a brief discussion concerning the postprocessing of spectral data.

Computers↗
PubMed1986

Robust spectral analysis of the EEG.

Robust methods for the spectral analysis of time series are briefly reviewed and seen to have applications in the field of EEG. After presenting two simple schemes for outliers (artifacts) generation and discussing their implications for estimation of the spectral density, the robust filtering algorithm of Kleiner et al. [J.R. Statist. Soc. Ser. B, 41: 313-351, 1979] is introduced and shown to work well for simulated data and for true EEG data containing artifacts. A new use of the robust methods for the detection of artifacts and possibly other transients in long-time EEG recordings is suggested and a preliminary implementation illustrated.

Electroencephalography↗
PubMed1984

EEG spectral power and coherence analysis in healthy full-term neonates.

Continuous, bipolar EEG recording was performed in nineteen healthy full-terms during quiet sleep (Q), REM sleep (R) and wakefulness (W). Spectral power was calculated for the bilateral fronto-central (FC), centro-parietal (CP), parieto-occipital (PO) and temporal (TT) derivations. Coherence was determined for the interhemispheric biFC, biCP, biPO and biTT locations, as well as for the left and right intrahemispheric FC-PO and TT-PO combinations. Power was highest in Q in the CP derivations at 0.4-12/s. Generally, power was higher in Q than in R. In most derivations, Q and R power ran parallel. W power behaviour was different. Up to 8/s, power in W was intermediate between Q and R power. From 10/s, it became highest, particularly in the TT and FC derivations. PO power was low. Highest coherence values occurred in the lowest frequencies. Up to 8/s, biFC, biCP and biPO interhemispheric coherences were highest in R and usually lowest during Q. In the alpha-beta range, highest coherences occurred during Q and lowest during R or W, without much variance between stages. BiPO coherences were remarkably high during all stages. BiTT and intrahemispheric coherences were low and less consistent. These data suggest high synchronization of theta-delta activity between biPO regions, particularly during R. We hypothesize active common generators of neonatal theta-delta activity in the posterior regions, indicating less autonomy and more interdependent behaviour of these areas especially during R.

Cerebral Cortex↗
PubMed1984

EEG-based multivariate statistical analysis of sleep stages.

All-night sleep of 5 healthy male subjects was scored on the basis of EEG (paper records), EMG and EOG into the stages of wakefulness, REM sleep and slow sleep stages 1-4 according to Rechtschaffen and Kales. Spectral analysis for 8 EEG channels was then performed and spectral parameters (total power and coherence for certain frequency bands) extracted. Stepwise linear discriminant analysis was applied to these spectral parameters to see how well the polygraphically defined sleep stages were recognized. While the results of each intrasubject analysis were satisfactory (error rates between 10 and 15%), a more detailed analysis revealed that most errors occur between nearby slow sleep stages or between S1 and REM or wakefulness. This is related to the fact that Rechtschaffen and Kales classify into discrete stages an essentially continuous process and this is, to some extent, arbitrary. This interpretation is supported by examples. Cross-classification of each subject on the basis of the remaining 4 increases the error rates drastically. A simple way of avoiding this effect, by standardizing the individual data, is shown to work nicely in this case.

Adult↗
PubMed1984

[Temporal dynamics of spectral EEG parameters during nocturnal sleep in healthy adults].

We recorded all-night sleep EEG's of six healthy male volunteers (age 23 to 29 years) from F3, F4, P3, P4, 01, 02, T3, T4 to Cz as reference electrode. Power and coherence spectra were calculated for ten frequency bands from 0-30 cps. We examined their changes through the different cycles of all sleep stages. In general there was a decrease in power as well as in coherence from the 1st to the 4th cycle of the different sleep stages with some variations depending on frequency and derivation. The highest power was most often in the 1st cycle in stages REM, 2, 3 and 4, whereas in stage 1 it was most often in the 2nd cycle. The trends in power from the 1st to the 4th cycle were similar for stages REM and 2 with a power decrease from the 1st to the 2nd cycle. In stage 1 there was most often a power increase between these two cycles. Coherence maxima were for stages REM, 1, 2 and 3 most often in the 1st cycle, for stage 4 most often in the 2nd cycle. The coherence trends were similar for stages 1 and 2 with a decrease most often from the 1st to the 2nd cycle. In stage REM the decline was more constantly found from the 2nd to the 3rd cycle. The power trends were more consistent than the coherence trends. We found more often similar power changes from the 1st to the 4th cycle of a specific sleep stage than similar coherence changes.

Adult↗
PubMed1983

[Application of beta-rhythm quantification to the treatment of epilepsy].

Abundance of beta activity in epileptic patients under clonazepam (Rivotril) treatment shows a significant correlation with the suppression of epileptic seizures and/or of epileptic EEG activity such as spikes and spike/wave complexes. In a longitudinal two-year study in 21 children with severe epilepsy treated with clonazepam, relative spectral peak power (RSPP) of beta activity was correlated with daily dose and serum concentration as well as with its effect on seizures and on epileptic EEG activity. Eight cases showed significant correlation of beta RSPP to daily dose, but only 4 to serum concentration. A significant (inverse) correlation was found between beta RSPP and the relative occurrence of epileptic EEG activity. Most illustrative were the individual follow-ups, with beta RSPP as an interesting additional parameter regarding the course of the epileptic condition. Quantitative measurement of beta activity by means of spectral analysis during long-term antiepileptic treatment promises important additional information on specific aspects in the individual case.

Benzodiazepinones↗
PubMed1983

[Spectral analysis of rapid (beta) EEG activity].

Spectral analysis techniques provide high resolution of the beta frequency range and, if a logarithmic transformation is used, also of lower level intensity. This is in contrast to the generally poor resolution of fast activity in the time domain, an effect which may be compared with that of a magnifying glass applied to beta activity. In the frequency domain several different types of beta activity may be distinguished, i.e., (1) narrow; (2) medium; (3) broad-band beta; (4) harmonic beta where the beta component shows a harmonic relationship to a lower frequency component; (5) complex beta as a mixture of 2 or more of the foregoing types; and (6) undefined beta, where the relative increase of beta power is the only assessable feature. If observed, significant coherence occurs most frequently between antero-posterior leads in activities of types 1 and 2, whereas coherence between hemispheres is generally low. Broad-band beta seems to be essentially non-coherent. Coherence of harmonic beta depends largely on the features of the underlying component. Bi-coherence analysis may be of help to further analysis of harmonic or complex beta activity.

Beta Rhythm↗
PubMed1983

Spectral analysis of all-night sleep EEG in healthy adults.

Power and coherence spectra were computed from all-night sleep EEG records in 6 healthy adult subjects. Derivations were from F3, F4, P3, P4, O1, O2, T3, and T4 to the vertex (Cz). Records were conventionally scored into sleep stages. Average power per sleep stage was maximal at frequencies 0.4-6 c/s in stage 4, at 6-10 c/s in either stage 3 or stage 4, at 12-14 c/s in stage 2 and at 14-30 c/s in stage 1. The average power range from highest values in the lowest frequency band to lowest values in the highest frequency band showed marked differences between sleep stages: It was lowest (12-14 dB) in stage 1, followed by stage 2 (20-22 dB), and stage 3 (16-28 dB), and largest in stage 4 (29-32 dB). REM sleep (15-16 sB) was between stage 1 and 2. The waking state showed an average power range of 11-15 dB. Alpha power at 8-10 c/s in occipital and parietal leads was remarkably constant during sleep, i.e. independent of sleep stage. Coherence showed maximal values at 2-8 c/s in REM sleep, at 8-12 c/s in stage 4, at 12-17 c/s in either stage 3 or 4, and at 17-30 c/s again in stage REM. There was significant coherence increase at 2-8 and 17-30 c/s from NREM to REM sleep, most pronounced between parietal to vertex derivations. Overall coherence between both occipital-to-vertex, or between occipital and parietal-to-vertex derivations, was essentially higher than in the other derivations. The results, essentially, give a comprehensive phenomenology of the dynamic spectral structure of all-night sleep EEG. They suggest that the different brain states during sleep (e.g. stage 1 NREM vs. REM) which are associated with different functions (e.g. hypnagogic hallucinations vs. dreams) differ in EEG spectral parameters if coherence is considered. Likewise, they suggest that studies of automatic sleep staging based exclusively on EEG spectral parameters appear promising.

Adult↗
PubMed1983

[Multivariate analysis of beta activity in clinical material].

Beta activity, as described by a number of parameters extracted from the power spectra, was submitted to several multivariate statistical techniques in search of interpretable subgroups. The difficulties of doing this on heterogeneous clinical material, as well as various shortcomings of the usual cluster analytical techniques, are outlined. Where longitudinal data are available, the analysis of beta activity may help in monitoring the effects of the therapy. Generally, the large variability between individuals and many sources of errors related to the processes of data collection and parameter extraction indicate that much work remains to be done before even a rough and clinically relevant typology of beta activity will become available.

Adolescent↗
PubMed1982

Transient EEG patterns during sleep in healthy newborns.

24 healthy full-term newborns underwent polygraphic recordings of EEG, EMG, EOG, ECG, abdominal and thoracic respiration during day-time-sleep. Transient EEG patterns (rhythmic alpha and beta activity, spikes/sharp waves and frontal sharp transients) were visually evaluated and quantified. Rhythmic alpha activity is not very prominent. It is found in all states of sleep and appears only as scattered waves and interrupted sequences. Their duration varies from 1 to 5 sec. Rhythmic beta activity is a feature of quiet and active-REM sleep, repeatedly in spindle-shaped formations. "Typical" sleep spindle activity, however, is very rare. In a few cases rhythmic beta activity also appears in the form of the delta brush pattern. Spikes/sharp waves are sporadic in appearance, non-repetitive in nature and occur mostly multifocal with parietal predominance. They are more frequent in quiet sleep (1 per 2.3 min on average) than in active-REM sleep (1 per 18 min), and were rare in transitional states of sleep (1 per hour). "Frontal sharp transients" show great variations of configuration. They occur most frequently in quiet sleep. Often they appear only unilaterally. Their voltage maximum is frontal, fronto-temporal or praecentral. They are more frequent in REM sleep after wakefulness than after quiet sleep. The individual as well as the sleep-state related frequencies of spike/sharp waves and "frontal sharp transients" tend to differ widely with a range of a factor 10. In some of the newborns these graphoelements are not observed at all during certain sleep phases.

Electroencephalography↗