Observer-Based Deconvolution of Deterministic Input in Coprime Multichannel Systems With Its Application to...

Area of Science:

• Cardiovascular physiology
• Systems biology
• Biomedical engineering

Background:

• Accurate central aortic blood pressure (BP) estimation is crucial for cardiovascular (CV) health and risk prediction.
• The CV system functions as a multichannel dynamical system, generating multiple BPs from a central aortic BP input.
• Noninvasive BP monitoring methods are essential for widespread CV health assessment.

Purpose of the Study:

• To develop and analyze an observer-based deconvolution approach for noninvasive central aortic BP estimation.
• To address the challenge of estimating a common input (central aortic BP) from multiple system outputs (peripheral BPs).
• To validate the proposed method using a case study involving peripheral arterial pulse waveforms.

Main Methods:

• Development of an unknown input observer (UIO) for deconvolution in coprime multichannel systems.
• Design of a universal algorithm for UIO implementation.
• Analysis of physical insights and performance limitations of the UIO algorithm.

Main Results:

• The UIO approach successfully estimated central aortic BP waveforms from peripheral pulse waveforms.
• The UIO method reduced root-mean-squared error (RMSE) by up to 27.5% compared to inverse filtering (IF).
• The UIO method reduced RMSE by up to 28.8% compared to peripheral arterial pulse scaling techniques.

Conclusions:

• The observer-based deconvolution approach offers a promising method for accurate noninvasive central aortic BP estimation.
• This technique has the potential to enhance cardiovascular risk prediction models.
• The UIO provides a significant improvement over conventional methods for BP waveform analysis.