Blind Deconvolution of the Aortic Pressure Waveform Using the Malliavin Calculus

Multichannel Blind Deconvolution (MBD) is a powerful tool particularly for the identification and estimation of dynamical systems in which a sensor, for measuring the input, is difficult to place. This paper presents an MBD method, based on the Malliavin calculus MC (stochastic calculus of variation...

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Bibliographic Details
Published in:Mathematical Problems in Engineering 2010-01, Vol.2010 (1), p.17-43-002
Main Authors: Abutaleb, Ahmed S., Waheed, M. El-Sayed, Elhamy, Nermeen M.
Format: Article
Language:English
Subjects:
Aorta
Blood pressure
Calculus
Calculus of variations
Deconvolution
Engineering
FIR filters
Methods
Monte Carlo simulation
Noise
Studies
Veins & arteries
Waveforms
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Summary:Multichannel Blind Deconvolution (MBD) is a powerful tool particularly for the identification and estimation of dynamical systems in which a sensor, for measuring the input, is difficult to place. This paper presents an MBD method, based on the Malliavin calculus MC (stochastic calculus of variations). The arterial network is modeled as a Finite Impulse Response (FIR) filter with unknown coefficients. The source signal central arterial pressure CAP is also unknown. Assuming that many coefficients of the FIR filter 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 proposed Malliavin calculus-based method. We have been able to deconvolve the measurements and obtain both the source signal and the arterial path or filter. The presented examples prove the superiority of the proposed method, as compared to conventional methods.
ISSN:1024-123X
1563-5147
DOI:10.1155/2010/102581