Multichannel Blind Deconvolution Using the Stochastic Calculus for the Estimation of the Central Arterial Pressure

A new tool for estimation of both the central arterial pressure and the unknown channel dynamics has been developed. Given two peripheral waveform measurements, this new signal processing algorithm generates two models that represent the distinct branch dynamic behavior associated with the measured...

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Bibliographic Details
Published in:Mathematical Problems in Engineering 2010-01, Vol.2010 (1), p.1026-1046-160
Main Authors: Abutaleb, Ahmed S., Waheed, M. El-Sayed, Elhamy, Nermeen M.
Format: Article
Language:English
Subjects:
Algorithms
Blinds
Blood pressure
Convolution
Deconvolution
Dynamical systems
Dynamics
Engineering
FIR filters
Impulse response
Mathematical analysis
Mathematical models
Methods
Noise
Pulmonary arteries
Signal processing
Studies
Time measurement
Veins & arteries
Waveforms
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Summary:A new tool for estimation of both the central arterial pressure and the unknown channel dynamics has been developed. Given two peripheral waveform measurements, this new signal processing algorithm generates two models that represent the distinct branch dynamic behavior associated with the measured signals. The framework for this methodology is based on a Multichannel Blind Deconvolution (MBD) technique that has been reformulated to use Stochastic Calculus (SC). The technique is based on MBD of dynamic system are mathematically analyzed, in order to reconstruct the common unobserved input within an arbitrary scale factor. The convolution process is modeled as a Finite Impulse Response (FIR) filter with unknown coefficients. The source signal is also unknown. Assuming that one of the FIR filter coefficients are time varying, we have been able to get accurate estimation results for the source signal, even though the filter order is unknown. The time varying filter coefficients have been estimated through the SC algorithm, and we have been able to deconvolve the measurements and obtain both the source signal and the convolution path. The positive results demonstrate that the SC approach is superior to conventional methods.
ISSN:1024-123X
1563-5147
DOI:10.1155/2010/602373