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

Abstract

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.