A novel electrical impedance function to estimate central aortic blood pressure waveforms. (July 2021)

• Record Type: Journal Article
• Authors: Sooriamoorthy, Denesh; Shanmugam, S. Anandan; Juman, M.A.
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Generalized transfer function -- N-point moving average -- Adaptive transfer function -- Electrical impedance function -- Mathematical function -- Blood pressure waveform; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102649
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Estimation of central aortic blood pressure waveform (CASP). Conversion of radial blood pressure waveform to aortic blood pressure waveform. Estimation of blood pressure waveform using Mathematical equation. Estimation of aortic blood pressure waveform using 0D modelling. Abstract: Background and objective: Central aortic blood pressure waveforms convey imperative information about cardiovascular risk, but these measurements are obtained invasively. The study aims to develop an electrical impedance function (EIF) to estimate the central (aortic) blood pressure waveform from the radial blood pressure waveform without the need of invasive procedures. Methods: This paper shows a method of using the four-element Windkessel model to derive an EIF using circuit analysis to estimate the central aortic blood pressure. The four elements were identified from radial waveforms by using the Nelder-Mead simplex algorithm and then used to construct the EIF to obtain the aortic pressure waveform, given the radial waveform. Results: Waveforms generated by EIF gave the lowest values for Root Mean Square Error (RMSE) (5.69) and Mean Average Percentage Error (MAPE) (0.0402) compared to the generalized transfer function (GTF) and N-point moving average (NPMA), and the lowest MAPE and a comparable RMSE when compared to the Adaptive Transfer Function (ATF). Moreover, EIF has a significantly lower computing time (0.008 ms) when compared with GTF, NPMA and ATF. Conclusions: The proposed function had comparable or better performance to existing methods when tested on both simulated and actual patient data, and was able to give a better signal shape retention at the dicrotic notch (99.94 % correlation), which is a crucial feature for cardiovascular disease detection. Significance: Overall the EIF performance validates its use in estimating central aortic pressure waveforms, making it potentially viable for better embedded systems for non-invasive central blood pressure monitoring due to its low computation time and high accuracy.