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M Jobert

Publications and source records attributed to M Jobert.

21 records · Page 2Linked to original sources

[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↗

[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↗

[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↗