Removing Baseline Drift in Pulse Waveforms by a Wavelet Adaptive Filter

This work designs a wavelet adaptive filter (WAF) to remove the baseline drift from pulse waveforms. The WAF consists of two parts: the transform algorithm based on discrete Meyer wavelet to decompose the pulse signal into eight frequency bands; the improved adaptive filter that uses the high-freque...

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Published in2008 2nd International Conference on Bioinformatics and Biomedical Engineering Vol. 2; pp. 2135 - 2137
Main Authors Cao, Dianguo, Liu, Changchun, Peng, Wang
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 2008
Subjects
Adaptive filters
Design automation
Discrete wavelet transforms
Frequency
Heart rate
Medical diagnostic imaging
Signal analysis
Testing
Wavelet transforms
Wiener filter
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Summary:This work designs a wavelet adaptive filter (WAF) to remove the baseline drift from pulse waveforms. The WAF consists of two parts: the transform algorithm based on discrete Meyer wavelet to decompose the pulse signal into eight frequency bands; the improved adaptive filter that uses the high-frequency components of the pulse signal as reference input and the original pulse waveform added baseline drift as primary input. The WAF is tested on our developed pulse diagnosis apparatus. The results both on simulated and real human pulse signals demonstrate that the proposed WAF outperforms traditional filters not only in removing baseline drift but in preserving the diagnostic information of pulse waveforms.
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ISBN:9781424417476
1424417473
ISSN:2151-7614
2151-7622
DOI:10.1109/ICBBE.2008.863