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Noninvasive continuous blood pressure estimation with fewer parameters based on RA-ReliefF feature selection and MPGA-BPN models. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Ma G, Chen Y, Zhu W, Zheng L, Tang H, Yu Y, Wang L. Evaluating and Visualizing the Contribution of ECG Characteristic Waveforms for PPG-Based Blood Pressure Estimation. MICROMACHINES 2022; 13:1438. [PMID: 36144060 PMCID: PMC9502729 DOI: 10.3390/mi13091438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 08/27/2022] [Accepted: 08/27/2022] [Indexed: 06/16/2023]
Abstract
Non-invasive continuous blood pressure monitoring is of great significance for the preventing, diagnosing, and treating of cardiovascular diseases (CVDs). Studies have demonstrated that photoplethysmogram (PPG) and electrocardiogram (ECG) signals can effectively and continuously predict blood pressure (BP). However, most of the BP estimation models focus on the waveform features of the PPG signal, while the peak value of R-wave in ECG is only used as a time reference, and few references investigated the ECG waveforms. This paper aims to evaluate the influence of three characteristic waveforms in ECG on the improvement of BP estimation. PPG is the primary signal, and five input combinations are formed by adding ECG, P wave, QRS complex, T wave, and none. We employ five common convolutional neural networks (CNN) to validate the consistency of the contribution. Meanwhile, with the visualization of Gradient-weighted class activation mapping (Grad-CAM), we generate the heat maps and further visualize the distribution of CNN's attention to each waveform of PPG and ECG. The heat maps show that networks pay more attention to the QRS complex and T wave. In the comparison results, the QRS complex and T wave have more contribution to minimizing errors than P wave. By separately adding P wave, QRS complex, and T wave, the average MAE of these networks reaches 7.87 mmHg, 6.57 mmHg, and 6.21 mmHg for systolic blood pressure (SBP), and 4.27 mmHg, 3.65 mmHg, and 3.73 mmHg, respectively, for diastolic blood pressure (DBP). The results of the experiment show that QRS complex and T wave deserves more attention and feature extraction like PPG waveform features in the continuous BP estimation.
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Affiliation(s)
- Gang Ma
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Yuhang Chen
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Wenliang Zhu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Lesong Zheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Hui Tang
- School of Electronics and Information Technology, Soochow University, Suzhou 215006, China
| | - Yong Yu
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Lirong Wang
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
- School of Electronics and Information Technology, Soochow University, Suzhou 215006, China
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Charlton PH, Paliakaitė B, Pilt K, Bachler M, Zanelli S, Kulin D, Allen J, Hallab M, Bianchini E, Mayer CC, Terentes-Printzios D, Dittrich V, Hametner B, Veerasingam D, Žikić D, Marozas V. Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet. Am J Physiol Heart Circ Physiol 2022; 322:H493-H522. [PMID: 34951543 PMCID: PMC8917928 DOI: 10.1152/ajpheart.00392.2021] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 12/07/2022]
Abstract
The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Research Centre for Biomedical Engineering, University of London, London, United Kingdom
| | - Birutė Paliakaitė
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Kristjan Pilt
- Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia
| | - Martin Bachler
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Serena Zanelli
- Laboratoire Analyze, Géométrie et Applications, University Sorbonne Paris Nord, Paris, France
- Axelife, Redon, France
| | - Dániel Kulin
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- E-Med4All Europe, Limited, Budapest, Hungary
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Magid Hallab
- Axelife, Redon, France
- Centre de recherche et d'Innovation, Clinique Bizet, Paris, France
| | | | - Christopher C Mayer
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dimitrios Terentes-Printzios
- Hypertension and Cardiometabolic Unit, First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Verena Dittrich
- Redwave Medical, Gesellschaft mit beschränkter Haftung, Jena, Germany
| | - Bernhard Hametner
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dave Veerasingam
- Department of Cardiothoracic Surgery, Galway University Hospitals, Galway, Ireland
| | - Dejan Žikić
- Faculty of Medicine, Institute of Biophysics, University of Belgrade, Belgrade, Serbia
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
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Lechat B, Scott H, Naik G, Hansen K, Nguyen DP, Vakulin A, Catcheside P, Eckert DJ. New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences. Front Neurosci 2021; 15:751730. [PMID: 34690688 PMCID: PMC8530106 DOI: 10.3389/fnins.2021.751730] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/16/2021] [Indexed: 01/07/2023] Open
Abstract
Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.
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Affiliation(s)
- Bastien Lechat
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Hannah Scott
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Ganesh Naik
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Kristy Hansen
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Duc Phuc Nguyen
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Andrew Vakulin
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Peter Catcheside
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Danny J Eckert
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
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Papaioannou TG, Oikonomou E, Lazaros G, Christoforatou E, Vogiatzi G, Tsalamandris S, Chasikidis C, Kalambogias A, Mystakidi VX, Galiatsatos N, Santouri M, Latsios G, Deftereos S, Tousoulis D. The influence of resting heart rate on pulse wave velocity measurement is mediated by blood pressure and depends on aortic stiffness levels: insights from the Corinthia study. Physiol Meas 2019; 40:055005. [DOI: 10.1088/1361-6579/ab165f] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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