Aortic blood pressure estimation: A hybrid machine-learning and cross-relation approach

Abstract
Aortic blood pressure is a vital signal that provides valuable medical information about cardiovascular health condition. Noninvasive measurement of this signal is very challenging, which motivates several researchers to develop mathematical approaches over the years to estimate the aortic pressure from peripheral measurements. Most of these approaches are limited in their performance as they fail to recover important features of the blood pressure signal. To overcome this issue, we investigate the application of machine-learning methods to estimate the aortic blood pressure from peripheral signals. In the absence of reasonably large datasets, we rely on pre-validated virtual databases to train our machine-learning models. To avoid model bias due to the lack of diversity and variability in these databases, we propose a hybrid approach that combines machine-learning models with the cross-relation blind estimation approach. On top of that, a sparse representation, coupled with a dictionary-learning approach, is employed to emphasize the characteristics of the aortic pressure signals and generate more meaningful outputs. Our results show that the proposed hybrid approach offers a reduction in the root-mean-squared error compared to pure machine-learning models and improvement compared to the cross-relation method. The proposed approach also shows a noticeable potency in capturing fine features of the aortic blood pressure signal.

Citation
Magbool, A., Bahloul, M. A., Ballal, T., Al-Naffouri, T. Y., & Laleg-Kirati, T.-M. (2021). Aortic blood pressure estimation: A hybrid machine-learning and cross-relation approach. Biomedical Signal Processing and Control, 68, 102762. doi:10.1016/j.bspc.2021.102762

Acknowledgements
The research reported in this publication is supported by King Abdullah University of Science and Technology (KAUST).

Publisher
Elsevier BV

Journal
Biomedical Signal Processing and Control

DOI
10.1016/j.bspc.2021.102762

Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S1746809421003591

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