The central aortic blood pressure signal is an important source of information that contains cues about the cardiovascular system condition. Measuring this pulse wave clinically is burdensome as it can be only measured invasively with a catheter. As a result, many mathematical tools have been proposed in the past few decades to reconstruct the aortic pressure signal from the peripheral pressure signals that are usually easier to obtain noninvasively. At the distal level, the blood pressure signal is not directly useful since factors, such as length and stiffness of the arteries, play roles in changing the shape of the pressure signal significantly.
In this thesis, multichannel blind system identification techniques are proposed to estimate the central pressure waveform which vary in their accuracy and complex ity. First, a simple linear method is applied by approximating the nonlinear arterial system as a linear timeinvariant system and applying the crossrelation approach.
Next, a more complicated nonlinear Wiener system is proposed to model the nonlinear arterial tree. Along with the channelâ€™s coefficients, the nonlinear functions are estimated using crossrelation and kernel methods.
Datadriven machine learning methods are tested to estimate the aortic pressure signals. In many cases, they suffer from underfitting problems. As a remedy, a hybrid machine learning and crossrelation approach is also proposed to add more robustness to the machine learning models. This hybrid approach is implemented by combining the crossrelation with any machine learning method, including deep learning approaches.
The various methods are tested using prevalidated virtual databases. The results
show that the linear method produces root mean squared errors between 3.40 mmHg and 6.24 mmHg depending on the crossrelation constraint and the equalization tech nique. On the other hand, the root mean squared errors associated with the nonlinear methods are between 3.76 mmHg and 4.22 mmHg and hence more stable. For the hybrid machine learning and crossrelation approach, applying the crossrelation and the dictionary learning reduce the root mean squared errors up o 67% comparing with the pure machine learning models.
Date of Award  Jul 2020 

Original language  English (US) 

Awarding Institution   Computer, Electrical and Mathematical Sciences and Engineering


Supervisor  Tareq AlNaffouri (Supervisor) 

 Blind estimation
 central aortic pressure
 peripheral pressure
 optimization
 Machine Learning
 Equalization