Why the study?
Electroencephalography data are highly prone to internal and external artifacts and signal distortions, requiring effective smoothing filters for medical diagnostics.
Smoothing filters such as smooth, median, and Savitzky-Golay improve the legibility of EEG data for medical diagnostics by reducing artifacts and signal distortions.
May aid EEG interpretation in practice; leaves open prospective validation of diagnostic impact.
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.
No takes yet. Share an insight, caveat, or question.
Kawala‐Sterniuk et al. (2020) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: