Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

Wu, Haiyan; Ji, Zhong; Li, Mengze

doi:10.3390/s19245543

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…This earlier study has very high correlation coefficients, 0.999 and 0.998, respectively, for the SBP and DBP. Another study by Wu et al [ 7 ] also used ECG and PPG with the hybrid ANN method, and produced 3.404 mmHg and 3.289 mmHg, respectively, for SBP and DBP. Meanwhile, a study by Eom et al [ 8 ] conducted an investigation of multiple input signals, ECG, PPG, and BCG, combined with CNN, Bi-GRU, and attention methods.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, electrocardiography (ECG) and PPG, with ANN and long short-term memory (LSTM) methods were applied for the evaluations [ 6 ]. Backpropagation with a genetic algorithm (GA) was also used for hemodynamics evaluation [ 7 ]. Meanwhile, another study utilized ballistocardiography (BCG) and ECG alongside the PPG with convolutional neural networks (CNN) and a gated recurrent units (GRU)-based technique [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Sadrawi

Lin²,

Lin³

et al. 2021

Sensors

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This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson’s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system—the systolic blood pressure (SBP) and diastolic blood pressures (DBP)—the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system—the systolic and diastolic pressures—the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Sadrawi

Lin²,

Lin³

et al. 2021

Sensors

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“…Current commercial wearables and WIoT technologies can monitor and collect only a small portion of the physiological data and biomarkers (see Fig. 4 ) [1] , [18] – [27] . The sensor modalities implemented to measure physiological symptoms at the onset of COVID-19 include (but are not limited to) the temperature, HR using an electrocardiogram (ECG); pulse plethysmography (PPG); HR variability using ECG and PPG; blood pressure using PPG; respiratory rate using ECG, PPG, and an accelerometer; oxygen saturation (SpO2) using PPG; sleep using an accelerometer; and cough using mechanical or piezoelectric sensing.…”

Section: Role Of Iot In Managing Covid-19mentioning

confidence: 99%

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

Firouzi

Farahani

Daneshmand

et al. 2021

IEEE Internet Things J.

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As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) -including Machine Learning (ML) and Big Data analyticsas well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common

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“…New optical wearables and RPM technologies have the potential to address many clinical scenarios related to the COVID-19 pandemic. Table 2 contains a brief summary of opportunities for remote and wearable optical technology development, both for COVID-19 and for the many Optical scattering FD-DOS, TD-DOS, SFDI, 40 OCT, 41 ESS Glucose Spectroscopy [42][43][44] Blood pressure PPG 45,46 Images of the eye, skin Ophthalmology, 47 OCT, 41 smartphone-based dermoscopy 48 Images of the cervix Colposcopy 49…”

Section: Areas Of Opportunity For the Biomedical Optics Community To mentioning

confidence: 99%

Perspective on the increasing role of optical wearables and remote patient monitoring in the COVID-19 era and beyond

Roblyer

2020

J. Biomed. Opt.

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Significance: The COVID-19 pandemic is changing the landscape of healthcare delivery in many countries, with a new shift toward remote patient monitoring (RPM). Aim: The goal of this perspective is to highlight the existing and future role of wearable and RPM optical technologies in an increasingly at-home healthcare and research environment. Approach: First, the specific changes occurring during the COVID-19 pandemic in healthcare delivery, regulations, and technological innovations related to RPM technologies are reviewed. Then, a review of the current state and potential future impact of optical physiological monitoring in portable and wearable formats is outlined. Results: New efforts from academia, industry, and regulatory agencies are advancing and encouraging at-home, portable, and wearable physiological monitors as a growing part of healthcare delivery. It is hoped that these shifts will assist with disease diagnosis, treatment, management, recovery, and rehabilitation with minimal in-person contact. Some of these trends are likely to persist for years to come. Optical technologies already account for a large portion of RPM platforms, with a good potential for future growth. Conclusions: The biomedical optics community has a potentially large role to play in developing, testing, and commercializing new wearable and RPM technologies to meet the changing healthcare and research landscape in the COVID-19 era and beyond.

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Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

Cited by 17 publications

References 42 publications

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

Perspective on the increasing role of optical wearables and remote patient monitoring in the COVID-19 era and beyond

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