Estimation Method of Medical and Biological Signal Features in "Human-Computer" Interface Systems

One of the most widely used methods for the analysis of biomedical signals is wavelet transformation. Covering a wide range of tasks, from compressing signals to finding correlations with mental functions, wavelet transformation algorithms have showed good results. Well-proven in electroencephalogra...

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Bibliographic Details
Published in2021 14th International Conference Management of large-scale system development (MLSD) pp. 1 - 4
Main Authors Turovskii, Ya.A., Borzunov, S.V., Vakhtin, A.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.09.2021
Subjects
Electrocardiography
Electroencephalography
Filtering algorithms
local extremums
Signal processing algorithms
Time series analysis
Wavelet analysis
Wavelet transforms
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Summary:One of the most widely used methods for the analysis of biomedical signals is wavelet transformation. Covering a wide range of tasks, from compressing signals to finding correlations with mental functions, wavelet transformation algorithms have showed good results. Well-proven in electroencephalography (EEG) and electrocardiography (ECG) data processing, wavelet transformation nevertheless requires further enhancement focused on improving the detection of signal features relevant in an experiment or clinical study. This paper describes the method of creating filters that identify the features of biomedical signals based on the structure of chains of local extremums in the matrix of wavelet transformation coefficients. The paper also presents a method of selecting characteristic components related to operator activity from signals when working with Brain-Computer Interface (BCI) systems. There was also made a comparison with other methods of signal detection.
DOI:10.1109/MLSD52249.2021.9600115