preprocessing for ATP project (idea by Bela in Nov. 2017):
problem: data from HF measurement often contains outliers and artefact profiles, which have to be removed before generating a consensus with MICA.
solution: machine learning based identification of hf outliers based on manual curated data (pos/neg training/testing data available)
preprocessing for ATP project (idea by Bela in Nov. 2017):
problem: data from HF measurement often contains outliers and artefact profiles, which have to be removed before generating a consensus with MICA.
solution: machine learning based identification of hf outliers based on manual curated data (pos/neg training/testing data available)