Shortening osteotomy of the proximal radius can decrease radiocapitellar contact pressures during axial loading of up to 250 N. Primary valgus stability is not relevantly influenced by this procedure. In few patients, shortening osteotomy may cause radioulnar impingement of the radial head at the distal edge of the lesser sigmoid notch due to an inclination angle of >20°. Shortening osteotomy might be a promising treatment option to decrease pain levels in case of isolated radiocapitellar osteoarthritis.
A postoperative stay in hospital is sufficient for 3 days, but should vary individually. On young children with symptomatic hyperplasia of the tonsils a lasertonsillotomy should be performed.
Objective
Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain–computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed.
Approach
Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths.
Main results
Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only.
Significance
The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.
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