Application of translation wavelet transform with new threshold function in pulse wave signal denoising

• Main Authors: Geng, X. (Author), Wang, Y. (Author), Yao, F. (Author), Zhang, H. (Author), Zhang, J. (Author), Zhang, Y. (Author)
• Format: Article
• Language: English
• Published: NLM (Medline) 2023
• Subjects: a new threshold function; algorithm; Algorithms; blood pressure; Blood Pressure; denoising method; heart rate; Heart Rate; human; Humans; Pulse wave; signal noise ratio; Signal-To-Noise Ratio; translation invariant wavelet transform; wavelet analysis; Wavelet Analysis
• Online Access: View Fulltext in Publisher

 BACKGROUND: The wrist pulse wave under the optimal pulse pressure plays an important role in detecting human body's physiological and pathological information. Wavelet threshold filtering is a common method for pulse wave de-noising. However, traditional filtering methods cannot smoothen the whole pulse wave well and highlight the details. OBJECTIVE: In view of this, an attempt is made in this paper to propose the pulse wave denoising algorithm for pulse wave under optimal pulse pressure according to the translation invariant wavelet transform (TIWT) and the new threshold function. METHODS: Firstly, by using hyperbolic tangent curve and combining the advantages of soft threshold function and hard threshold function, the new threshold function is derived. Secondly, based on the TIWT, pseudo-Gibbs phenomenon gets suppressed. RESULTS: The experiments show that in comparison to the traditional wavelet filtering algorithm, the novel algorithm can better maintain the pulse wave geometric characteristics and has a higher signal to noise ratio (SNR). CONCLUSION: The TIWT with improved new threshold compensates the shortcomings of the traditional wavelet threshold denoising methods in a better way. It lays a foundation for extracting time-domain characteristics of pulse wave.