Computational assessment of model-based wave separation using a database of virtual subjects |
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| Affiliation: | 1. Center for Health & Bioresources, AIT Austrian Institute of Technology, Vienna, Austria;2. Institute for Analysis and Scientific Computing, Vienna University of Technology, Vienna, Austria;1. Department of Mechanical & Industrial Engineering, Montana State University, United States;2. Department of Cell Biology & Neurosciences, Montana State University, United States;3. Department of Orthopaedics & Sports Medicine, University of Washington, United States;1. Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands;2. Department of Orthopedic Surgery, State University of New York, Upstate Medical University, Syracuse, NY, USA;3. University of Twente, Laboratory for Biomechanical Engineering, Faculty of Engineering Technology, Enschede, The Netherlands;1. Department of Medicine (DIMED), Geriatrics Division, University of Padova, Italy;2. Department of Geriatrics, Azienda Sanitaria dell''Alto Adige, Bolzano, Italy;3. Emergency Department, Azienda Sanitaria dell''Alto Adige, Bolzano, Italy;4. National Research Council, Institute of Neuroscience, Aging Branch, Padova, Italy;1. Rehabilitation Sciences Institute, University of Toronto, 500 University Avenue, Toronto, ON, M5G 1V7, Canada;2. Toronto Rehabilitation Institute – University Health Network, Lyndhurst Centre, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada;3. Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Room 407, Toronto, ON, M5S 3G9, Canada;4. Department of Mechanical Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, 9211-116 Street NW, Room 10-368, Edmonton, AB, T6G 1H9, Canada;5. Institute of Physical Activity and Sport Sciences, Cruzeiro do Sul University, Rua Galvão Bueno, 868, 13o. andar, Bloco B, Liberdade, Sao Paulo, SP, 01506-000, Brazil;6. Department of Mechanical Engineering, Federal University of Santa Catarina, Caixa Postal 476 – Campus Universitário – Trindade, 88040-900, Florianópolis, SC, Santa Catarina, Brazil;7. Department of Physical Therapy, University of Toronto, 500 University Avenue, Toronto, ON, M5G 1V7, Canada;1. Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA;2. PERCRO Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, via Alamanni 13b, 56010 Ghezzano, San Giuliano Terme, Pisa, Italy;3. Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA |
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| Abstract: | The quantification of arterial wave reflection is an important area of interest in arterial pulse wave analysis. It can be achieved by wave separation analysis (WSA) if both the aortic pressure waveform and the aortic flow waveform are known. For better applicability, several mathematical models have been established to estimate aortic flow solely based on pressure waveforms. The aim of this study is to investigate and verify the model-based wave separation of the ARCSolver method on virtual pulse wave measurements.The study is based on an open access virtual database generated via simulations. Seven cardiac and arterial parameters were varied within physiological healthy ranges, leading to a total of 3325 virtual healthy subjects. For assessing the model-based ARCSolver method computationally, this method was used to perform WSA based on the aortic root pressure waveforms of the virtual patients. As a reference, the values of WSA using both the pressure and flow waveforms provided by the virtual database were taken.The investigated parameters showed a good overall agreement between the model-based method and the reference. Mean differences and standard deviations were −0.05 ± 0.02 AU for characteristic impedance, −3.93 ± 1.79 mmHg for forward pressure amplitude, 1.37 ± 1.56 mmHg for backward pressure amplitude and 12.42 ± 4.88% for reflection magnitude.The results indicate that the mathematical blood flow model of the ARCSolver method is a feasible surrogate for a measured flow waveform and provides a reasonable way to assess arterial wave reflection non-invasively in healthy subjects. |
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| Keywords: | Pulse wave analysis Wave separation analysis Arterial wave reflection Blood flow model Virtual database |
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