Biomedical subjects

M Jobert

Publications and source records attributed to M Jobert.

5 recordsLinked to original sources

PubMed1992

Pattern recognition by matched filtering: an analysis of sleep spindle and K-complex density under the influence of lormetazepam and zopiclone.

The evaluation of sleep EEG patterns is mostly accomplished by visual analysis. With modern personal computers however, it is possible to perform signal detection within a reasonable length of time automatically. This paper presents a method for signal processing based on matched filtering. This allows the detection of sleep spindles and K-complexes in a sleep EEG recording with a high degree of accuracy. First the technique is described, and the results of a validation study based on the comparison of visual evaluations and computer analysis are presented. Thereafter, results of an application study are presented. Sleep spindle and K-complex density under the influence of lormetazepam and zopiclone were examined. Under both medications sleep spindle density increased while K-complex density decreased. Computation of Pearson's correlation coefficients demonstrated that the interindividual sleep spindle and K-complex variations under both treatments are highly correlated. The data suggest that lormetazepam and zopiclone, although chemically different, have a similar mode of action and display comparable effects on the sleep EEG.

Adult↗

PubMed1992

Topographical analysis of sleep spindle activity.

There is evidence for two types of sleep spindle activity, one with a frequency of about 12 cycles/s (cps) and the other of about 14 cps. Visual examination indicates that both spindle types occur independently, whereby the 12-cps spindles are more pronounced in the frontal and the 14-cps spindles in the parietal region. The purpose of this paper is to provide more information about the exact topography of these patterns. First the occurrence of distinct signals in anterior and posterior brain regions was verified using pattern recognition techniques based on matched filtering. Thus the existence of two distinct sources of activity located in the frontal and parietal region of the brain, respectively, was demonstrated using EEG frequency mapping. Evaluation of sleep recordings showed high stability both in the frequency and location of the presumed spindle generators across sleep. Pharmacological effects of lormetazepam and zopiclone on both spindle types were investigated. Both substances enhanced the sleep spindle activity recorded from the frontal and parietal electrodes, but this increase was more pronounced in the parietal brain region.

Adult↗

PubMed1991

[Pattern recognition techniques in sleep polygraphy].

The evaluation of EEG-patterns is usually accomplished by visual analysis. Nowadays however, even personal computers are fast enough for an efficient pattern recognition of EEG signals. Using sleep spindles and K-complexes as examples, our aim was to demonstrate how patterns can be detected in an EEG signal with a high degree of accuracy. Furthermore, recognition of K-complexes has been improved by applying an additional "adaptive algorithm" allowing individual adjustments to the signal's form and amplitude.

Algorithms↗

PubMed1989

[The effect of age on sleep spindle and K complex density].

The amount of sleep spindles and K-complexes shows a great interindividual variety of combinations, such as many or few sleep spindles and K-complexes respectively. There seems to be no direct correlation between the amount of sleep spindles and K-complexes intraindividually. In our unselected population - age range between 18 and 77 years - the mean sleep spindle density is at 2.59 +/- 1.85/min and the mean K-complex density at 1.96 +/- .96/min stage 2. The diffuse individual distribution, however, does not reflect the age factor involved. The sleep spindle and K-complex density were practically half the amount for the age group above 50 years as compared to the age group of less than 30 years.

Adolescent↗

PubMed1989

[A system for continuous digitizing and evaluation of 32 biosignals from all-night sleep leads].

In all-night sleep recordings usually 12 to 16 channel electroencephalographs are used to record the electrical activity of the brain. A detailed analysis of EEG sleep activity, however, requires the inclusion of at least 19 electrodes placed according to the international 10-20 system in order to compare the variations of the activities in different brain areas. In addition polygraphic parameters such as ECG, respiration and actogram, to mention just a few, have to be recorded depending on the type of study. Therefore the number of recording channels has to be increased for a complete polygraphic investigation. We developed a 32 channel unit with a personal computer and corresponding hardware interfaces allowing the continuous digitalization of up to 32 bioelectrical signals throughout the whole night (8 hours). The recorded data can be presented graphically and evaluated according to the usual methods such as power spectrum, coherence and periodicity analysis. Additionally the use of algorithms for pattern detection permits automatic analysis of EEG segments regarding particular sleep patterns e.g. sleep spindles and K-complexes.

Electroencephalography↗