A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG)
A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular b...
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| Veröffentlicht in: | Journal of cardiovascular translational research 2024-06, Vol.17 (3), p.669-684 |
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| container_title | Journal of cardiovascular translational research |
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| creator | Alam, Javed Khan, Mohammad Firoz Khan, Meraj Alam Singh, Rinky Mundazeer, Mohammed Kumar, Pramod |
| description | A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular because of its important applications in the evaluation of cardiac activity, variations in venous blood volume, blood oxygen saturation, blood pressure and heart rate variability, etc. In this study, we provide a comprehensive analysis of the extraction of various physiological parameters using PPG waveforms. In addition, we focused on the role of machine learning (ML) models used for the estimation of blood pressure and hypertension classification based on PPG waveforms to make future research and innovation recommendations. This study will be helpful for researchers, scientists, and medical practitioners working on PPG waveforms for monitoring, screening, and diagnosis, as a comparative study or reference.
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| doi_str_mv | 10.1007/s12265-023-10462-x |
| format | Article |
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Trans. Res</stitle><addtitle>J Cardiovasc Transl Res</addtitle><date>2024-06-01</date><risdate>2024</risdate><volume>17</volume><issue>3</issue><spage>669</spage><epage>684</epage><pages>669-684</pages><issn>1937-5387</issn><issn>1937-5395</issn><eissn>1937-5395</eissn><abstract>A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular because of its important applications in the evaluation of cardiac activity, variations in venous blood volume, blood oxygen saturation, blood pressure and heart rate variability, etc. In this study, we provide a comprehensive analysis of the extraction of various physiological parameters using PPG waveforms. 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| ispartof | Journal of cardiovascular translational research, 2024-06, Vol.17 (3), p.669-684 |
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| subjects | Biomedical Engineering and Bioengineering Biomedicine Blood Pressure - physiology Cardiology Diagnosis, Computer-Assisted Heart Rate Human Genetics Humans Hypertension - diagnosis Hypertension - physiopathology Machine Learning Medicine Medicine & Public Health Photoplethysmography Predictive Value of Tests Prognosis Reproducibility of Results Review Signal Processing, Computer-Assisted |
| title | A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG) |
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