Open AccessFingertip pulse rate variability extraction based on extreme-point symmetric mode decomposition
Author(s) -
Zhao Wei,
Min Li,
Youyuan Tang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2246/1/012085
Subject(s) - noise (video) , extreme point , interference (communication) , filter (signal processing) , waveform , mode (computer interface) , computer science , extraction (chemistry) , pattern recognition (psychology) , feature extraction , point (geometry) , algorithm , acoustics , control theory (sociology) , mathematics , artificial intelligence , physics , telecommunications , radar , computer vision , channel (broadcasting) , chemistry , geometry , control (management) , chromatography , combinatorics , image (mathematics) , operating system
To solve shortcomings of being sensitive to noise and waveform when using threshold method to extract pulse rate variability (PRV) at this stage, a method using extreme-point symmetric mode decomposition to extract PRV signals from pulse waves with noise is proposed. A Butterworth filter is used to remove the baseline drift, the DC mode and power frequency noise, which can effectively avoid noise interference when identifying peak points. The pre-processed data is decomposed by extreme-point symmetric mode decomposition to select the corresponding mode of the main wave, thereby reducing the difficulty of feature point extraction. Compared with the threshold method, the extraction method proposed in this paper is more intuitive and can self-adaptively select the best decomposition layer. This method is suitable for the extraction of fingertip PRV under complex noise and some disease statuses.