title: Recovering signals in physiological systems with large datasets.
abstract: In many physiological studies, variables of interest are not directly accessible, requiring that they be estimated indirectly from noisy measured signals. Here, we introduce two empirical methods to estimate the true physiological signals from indirectly measured, noisy data. The first method is an extension of Tikhonov regularization to large-scale problems, using a sequential update approach. In the second method, we improve the conditioning of the problem by assuming that the input is uniform over a known time interval, and then use a least-squares method to estimate the input. These methods were validated computationally and experimentally by applying them to flow-through respirometry data. Specifically, we infused CO2 in a flow-through respirometry chamber in a known pattern, and used the methods to recover the known input from the recorded data. The results from these experiments indicate that these methods are capable of sub-second accuracy. We also applied the methods on respiratory data from a grasshopper to investigate the exact timing of abdominal pumping, spiracular opening, and CO2 emission. The methods can be used more generally for input estimation of any linear system.
authors.affiliation: Department of Biomedical Engineering and Mechanics, Virginia Tech Blacksburg, Blacksburg, VA 24061, USA Department of Mathematics, Virginia Tech Blacksburg, Blacksburg, VA 24061, USA hpendar@vt.edu.
authors.forename: Hodjat
authors.identifier: http://orcid.org/0000-0001-8853-0062
authors.initials: H
authors.lastname: Pendar
authors.affiliation: Department of Biomedical Engineering and Mechanics, Virginia Tech Blacksburg, Blacksburg, VA 24061, USA.
authors.forename: John J
authors.initials: JJ
authors.lastname: Socha
authors.affiliation: Department of Mathematics, Virginia Tech Blacksburg, Blacksburg, VA 24061, USA.
authors.forename: Julianne
authors.initials: J
authors.lastname: Chung
keywords: Deconvolution; Flow-through respirometry; Ill-conditioned inverse problems; Input estimation