Key result
A multichannel blind system identification technique applied to peripheral pressure waveforms accurately derived aortic pressure in seven human hemodynamic datasets.
Why the study?
Can aortic pressure be accurately estimated using a multichannel blind system identification approach on peripheral pressure waveforms?
Can aortic pressure be accurately estimated using a multichannel blind system identification approach on peripheral pressure waveforms?
A novel multichannel blind system identification method can accurately estimate central aortic pressure noninvasively from two peripheral pressure waveforms.
May facilitate noninvasive aortic pressure estimation; leaves open clinical adoption pending larger validation studies.
Aortic pressure () is important for diagnosis of cardiovascular diseases, but it cannot be directly measured by noninvasive means. We present a method for its estimation by modeling arterial system as multichannel Weiner system with linear finite impulse response filter accounting for larger arteries transmission channel and nonlinear memoryless function block accounting for all nonlinearities due to narrowing of arteries, branching and visco-elastic forces. With this structure when pressure waveforms are measured from two distinct peripheral locations, multichannel blind system identification (MBSI) technique can be used to estimate common input pressure signal or . Nonlinear MBSI method was employed on previously acquired human hemodynamic measurements (seven datasets); results show can be accurately derived. This method by nature is self-calibrating to account for any interpersonal, along with intrapersonal, vascular dynamics inconstancy. Besides Pa estimation, the proposed MBSI method also allows extraction of system dynamics for vascular channels.
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Patel et al. (2017) studied Cardiovascular diseases (n=7). Multichannel blind system identification (MBSI) technique was evaluated on Aortic pressure (Pa) estimation accuracy. A multichannel blind system identification technique applied to peripheral pressure waveforms accurately derived aortic pressure in seven human hemodynamic datasets.
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