Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes
Aleksandra Kawala-Sterniuk,
Michal Podpora,
Mariusz Pelc,
Monika Blaszczyszyn,
Edward Jacek Gorzelanczyk,
Radek Martinek,
Stepan Ozana
Affiliations
Aleksandra Kawala-Sterniuk
Faculty of Electrical Engineering, Opole University of Technology, Automatic Control and Informatics, 45-758 Opole, Poland
Michal Podpora
Faculty of Electrical Engineering, Opole University of Technology, Automatic Control and Informatics, 45-758 Opole, Poland
Mariusz Pelc
Faculty of Electrical Engineering, Opole University of Technology, Automatic Control and Informatics, 45-758 Opole, Poland
Monika Blaszczyszyn
Faculty of Physical Education and Physiotherapy, Opole University of Technology, 45-758 Opole, Poland
Edward Jacek Gorzelanczyk
Department of Theoretical Basis of BioMedical Sciences and Medical Informatics, Nicolaus Copernicus University, Collegium Medicum, 85-067 Bydgoszcz, Poland
Radek Martinek
Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, FEECS, Ostrava-Poruba 708 00, Czech Republic
Stepan Ozana
Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, FEECS, Ostrava-Poruba 708 00, Czech Republic
This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky−Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.
