Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes.

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ID: 92843
2020
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Abstract
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.
Reference Key
kawalasterniuk2020comparisonsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kawala-Sterniuk, Aleksandra;Podpora, Michal;Pelc, Mariusz;Blaszczyszyn, Monika;Gorzelanczyk, Edward Jacek;Martinek, Radek;Ozana, Stepan;
Journal sensors
Year 2020
DOI
E807
URL
Keywords
electroencephalography signal processing savitzky–golay filter. smoothing filters Children physical activity grit

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