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    Blind Estimation of Central Blood Pressure Waveforms from Peripheral Pressure Signals

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    Thumbnail
    Name:
    AhmedMagboolThesis.pdf
    Size:
    2.042Mb
    Format:
    PDF
    Description:
    Final Thesis
    Embargo End Date:
    2021-07-12
    Download
    Type
    Thesis
    Authors
    Magbool, Ahmed cc
    Advisors
    Al-Naffouri, Tareq Y. cc
    Committee members
    Al-Naffouri, Tareq Y. cc
    Laleg-Kirati, Taous-Meriem cc
    Gao, Xin cc
    Program
    Electrical Engineering
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-07
    Embargo End Date
    2021-07-12
    Permanent link to this record
    http://hdl.handle.net/10754/664203
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2021-07-12.
    Abstract
    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, multi-channel 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 time-invariant system and applying the cross-relation approach. Next, a more complicated nonlinear Wiener system is proposed to model the nonlinear arterial tree. Along with the channel’s coefficients, the nonlinear functions are estimated using cross-relation and kernel methods. Data-driven 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 cross-relation approach is also proposed to add more robustness to the machine learning models. This hybrid approach is implemented by combining the cross-relation with any machine learning method, including deep learning approaches. The various methods are tested using pre-validated 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 cross-relation 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 cross-relation approach, applying the cross-relation and the dictionary learning reduce the root mean squared errors up o 67% comparing with the pure machine learning models.
    Citation
    Magbool, A. (2020). Blind Estimation of Central Blood Pressure Waveforms from Peripheral Pressure Signals. KAUST Research Repository. https://doi.org/10.25781/KAUST-75Q07
    DOI
    10.25781/KAUST-75Q07
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-75Q07
    Scopus Count
    Collections
    Theses; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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