Using wavelet transform for feature extraction from EEG signal

Manual evaluation of long-term EEG recordings is very tedious, time consuming, and subjective process. The aims of automated processing are on one side to ease the work of medical doctors and on the other side to make the evaluation more objective. This paper addresses the problem of computer-assisted steep staging. It describes ongoing research in this area. The proposed solution comprises several consecutive steps, namely EEG signal pre-processing, feature extraction, feature normalization, and application of decision trees for classification. The work is focused on the feature extraction step that is regarded as the most important one in the classification process.