Recovering signals in physiological systems with large datasets

Biology Open

Hodjat Pendar¹  John J. Socha¹  Julianne Chung² 

[1] Department of Biomedical Engineering and Mechanics, Virginia Tech Blacksburg, Blacksburg, VA 24061, USA;Department of Mathematics, Virginia Tech Blacksburg, Blacksburg, VA 24061, USA;

关键词: Input estimation;    Deconvolution;    Ill-conditioned inverse problems;    Flow-through respirometry;   

DOI  :  10.1242/bio.019133

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown