Sleep scoring needs computational assistance to reduce execution time and to assure high quality. In this pilot study a semi-automatic K-Complex detection algorithm was developed using wavelet transformation to identify pseudo-K-Complexes and various feature thresholds to reject false positives. The algorithm was trained and tested on sleep EEG from two databases to enhance its general applicability. When testing on data from subjects from the DREAMS© database, a mean true positive rate of 74 % and a positive predictive value of 65 % were achieved. After adjusting a few thresholds to adapt to the second database, the Danish Center for Sleep Medicine, a similar performance was achieved. The algorithm performs at the level of the State of the Art and surpasses the inter-rater agreement rate.