Noninvasive estimation of aortic pressure waveform based on simplified Kalman filter and dual peripheral artery pressure waveforms. (June 2022)

• Record Type: Journal Article
• Authors: Liu, Wenyan; Du, Shuo; Zhou, Shuran; Mei, Tiemin; Zhang, Yuelan; Sun, Guozhe; Song, Shuang; Xu, Lisheng; Yao, Yudong; Greenwald, Stephen E.
• Is Part Of: Computer methods and programs in biomedicine. Volume 219(2022)
• Publication Date: 2022-06
• Subjects: Aortic pressure -- Peripheral artery pressure -- Simplified Kalman filter -- Canonical correlation analysis -- Signal-to-noise ratio -- Noise-tolerance; 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.2022.106760
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The article proposes a novel SKF approach for reconstructing the central aortic pressure waveform from dual peripheral artery pressure waveforms. The main aim is to establish a cross-relation with the peripheral pressure measured noninvasively, and to use SKF's iterative optimization approach to derive an adaptive transfer function. The adaptive transfer function provides a patient-specific method to meeting the demand for a clinically useful way to reliably estimate central aortic pressure from non-invasive peripheral pressure measurements. The performance of the proposed method is superior to that of other state-of-the-art methods. Abstract: Background and Objective: Aortic pressure (Pa ) is important for the diagnosis of cardiovascular disease. However, its direct measurement is invasive, not risk-free, and relatively costly. In this paper, a new simplified Kalman filter (SKF) algorithm is employed for the reconstruction of the Pa waveform using dual peripheral artery pressure waveforms. Methods: Pa waveforms obtained in a previous study were collected from 25 patients. Simultaneously, radial and femoral pressure waveforms were generated from two simulation experiments, using transfer functions. In the first, the transfer function is a known finite impulse response; and in the second, it is derived from a tube-load model. To analyze the performance of the proposed SKF algorithm, variable amounts of noise were added to the observed output signal, to give a range of signal-to-noise ratios (SNRs). Additionally, central aortic, brachial and femoral pressure waveforms were simultaneously collected from 2 Sprague-Dawley rats and the measured and reconstructed Pa waveforms were compared. Results: The proposed SKF algorithm outperforms canonical correlation analysis (CCA), which is the current state-of-the-art blind system identification method for the non-invasive estimation of central aortic blood pressure. It is also shown that the proposed SKF algorithm is more noise-tolerant than the CCA algorithm over a wide range of SNRs. Conclusion: The simulations and animal experiments illustrate that the proposed SKF algorithm is accurate and stable in the face of low SNRs. Improved methods for estimating central blood pressure as a measure of cardiac load adds to their value as a prognostic and diagnostic tool.