The pulse waveform quantification method basing on contour and derivative. (June 2022)

• Record Type: Journal Article
• Authors: Jin, Ji; Zhang, Haiying; Geng, Xingguang; Zhang, Yitao; Ye, Tianchun
• Is Part Of: Computer methods and programs in biomedicine. Volume 220(2022)
• Publication Date: 2022-06
• Subjects: Pulse wave -- Quantification method -- Quantification parameter -- Kalman filter -- Derivative; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106784
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel quantification method for pulse waveform in the entire cardiac cycle involves six quantification parameters. Two new quantification parameters k1 and k2, display the waveform change caused by the superimposition of wave reflection. Multi parameters were fused by kalman filter to obtain an optimal estimation, which as a relative value can quantity the change of waveform about physiological information. Abstract: Objective: Pulse waveform contains abundant physiological and pathological information. The condition of surrounding arteries can be reflected sensitively by the contour and derivative changes of pulse waves. In order to express these changes objectively, the pulse wave needs to be quantified. Methods: This study provides a novel quantification method for pulse waveform in the entire cardiac cycle. It involves two new quantification parameters k1 and k2 to display the waveform change caused by the superimposition of wave reflection in the systolic reflex period, which is the most significant changes period. In this method, multi parameters were fused by Kalman filter to obtain an optimal estimation, involving the new parameters and other parameters: k0 for the early systolic period, C1 and C2 for diastole period, and K for pulse pressure. Results: Use correlation analysis to verify the effectiveness of new parameters that the coefficient is 0.7 between them and the typical augmentation index ( AIx ). The quantification results of 462 single-cycle pulse waves have consistent change trends with aging in 25–75 different age groups. For respiration analysis, the correlation coefficients are all greater than 0.6, even achieved 0.8 in six multi-cycle data between Kalman optimal estimation and breath wave. Conclusion: This method has quantified the waveform change with physiological status, and these quantification parameters can display the detail of each period. Significance: It will be used to verify waveform recognition accuracy and has a vast potential to detect diseases.