Automatic K-complexes detection in sleep EEG recordings using likelihood thresholds.

In this paper, we present an automatic method for K-complexes detection based on features extraction and the use of fuzzy thresholds. The validity of our process was examined on the basis of two visual K-complexes scorings performed on 5 excerpts of 30 minutes. Results were investigated through all different sleep stages. The algorithm provides global true positive rates of 61.72% and 60.94%, respectively with scorer 1 and scorer 2. The false positive proportions (compared to the total number of visually scored K-complexes) are of 19.62% and 181.25%, while the false positive rates estimated on a one 1 second resolution are only of 0.53% and 1.53%. These results suggest that our approach is completely suitable since its performances are similar to those of the human scorers.