A wavelet-based cascaded adaptive filter outperformed traditional filters in removing baseline drift and preserving diagnostic information in simulated and real pulse waveforms.
Does a wavelet-based cascaded adaptive filter improve baseline drift removal and preserve diagnostic information in pulse waveforms compared to traditional filters?
A novel wavelet-based cascaded adaptive filter effectively removes baseline drift from pulse waveforms while preserving diagnostic information better than traditional filters.
Absolute Event Rate: 0% vs 0%
This paper presents an energy ratio-based method and a wavelet-based cascaded adaptive filter (CAF) for detecting and removing baseline drift from pulse waveforms. Experiments on 50 simulated and five hundred real pulse signals demonstrate that this CAF outperforms traditional filters both in removing baseline drift and in preserving the diagnostic information of pulse waveforms.
Xu et al. (Tue,) reported a other. A wavelet-based cascaded adaptive filter outperformed traditional filters in removing baseline drift and preserving diagnostic information in simulated and real pulse waveforms.