| Abstrakt: |
Bioelectric signals belong to weak low-frequency signals with strong noise, therefore it is necessary to filter out power frequency interference. In order to ensure the accuracy and effectiveness of the filtering during power frequency offset, local outlier factor based on frequency density, and combines empirical mode decomposition was proposed to carry out adaptive denoising of signals. Firstly, the local outlier factor was used in the frequency domain by the short-time Fourier transform, and the frequency offset and the offset time and frequency were found by FLOF. Secondly, the signal was segmented according to the offset time, and the average instantaneous power frequency within the segment was used as the actual power frequency within the segment. Finally, each signal segment was decomposed by EMD to generate multiple local feature components of different time scales. More useful information could be reserved only for the component filtering containing power frequency signals. The frequency estimation accuracy of this method was high, and the SNR, RMSE, and SIM were improved after filtering in different dB. Taking -30 dB as an example, compared with the least mean square error filtering and recursive least squares filtering, the SNR increases by 16.266 and 7.671 dB, the RMSE decreased by 16.017 and 4.388 dB, and the SIM increased by 0.200 and 0.013. It proved that the filtering effect in this study was better than the conventional adaptive filter. [ABSTRACT FROM AUTHOR] |
