Bayesian source separation of electrical bioimpedance signals. (May 2021)

• Record Type: Journal Article
• Authors: Pichler, Christof; Ranftl, Sascha; Heller, Arnulf; Arrigoni, Enrico; von der Linden, Wolfgang
• Is Part Of: Biomedical signal processing and control. Volume 67(2021)
• Publication Date: 2021-05
• Subjects: Uncertainty quantification -- Bayesian probability theory -- Source separation -- Electrical bioimpedance -- Impedance cardiography; 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.2021.102541
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: Rigorous and consistent theory for electrical bioimpedance signal source separation. Quantifies uncertainties of signal component reconstructions and parameter estimates. Components may be recovered with very low signal-to-noise ratio. Serves as basis to quantify reliability of estimates of hemodynamic parameters. Can detect heart arrhythmias. Abstract: For physicians, it is often crucial to monitor hemodynamic parameters to provide appropriate treatment for patients. Such hemodynamic parameters can be estimated via electrical bioimpedance (EBI) signal measurements. Time dependent changes of the measured EBI signal occur due to several different phenomena in the human body. Most of the time one is just interested in a single component of the EBI signal, such as the part caused by cardiac activities, wherefore it is necessary to decompose the EBI signal into its different source terms. The changes of the signal are mostly caused by respiration and cardiac activity (pulse). Since these fluctuations are periodic in sufficiently small time windows, the signal can be approximated by a harmonic series with two different fundamental frequencies and an unknown number of higher harmonics. In this work, we present Bayesian Probability Theory as the adequate and rigorous method for this decomposition. The proposed method allows, in contrast to other methods, to consistently identify the model-function, compute parameter estimates and predictions, and to quantify uncertainties. Further, the method can handle a very low signal-to-noise ratio. The results suggest that EBI-based estimation of hemodynamic parameters and their monitoring can be improved and its reliability assessed.