Towards Robust Blood Pressure Estimation From Pulse Wave Velocity Measured by Photoplethysmography Sensors

Byfield, Richard; Miller, Morgan; Miles, Jonathan; Guidoboni, Giovanna; Lin, Jian

doi:10.1109/jsen.2021.3134890

Cited by 19 publications

(1 citation statement)

References 30 publications

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“…The error rate was stable at about 5% in a small experimental cohort, and the error rate referred to the ratio of the prediction error to the true BP value. The signal acquirement by Byfield et al [ 40 ] is similar to that designed by Kim et al, and the time interval of the two sites PPG was subjected to a Gaussian regression model for blood pressure prediction. Otherwise, Tabei et al [ 41 ] synchronously acquired the PPG signals at the index of the left and right hand through the cameras of two mobile phones and sequentially calculated the PTT to estimate blood pressure.…”

Section: Methodsmentioning

confidence: 99%

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin

Wang

et al. 2022

BioMed Research International

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Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.

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Section: Methodsmentioning

confidence: 99%

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin

Wang

et al. 2022

BioMed Research International

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Deep Learning‐Coupled Metabolic Heat Integrated Sensing System for Noninvasive Continuous Monitoring of Blood Glucose

Wang,

Yao,

Liu

et al. 2024

Advanced Intelligent Systems

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The dynamic and continuous monitoring of blood glucose (BG) concentration is crucial for the health management of diabetic patients. Despite its importance, significant challenges remain in the development of effective BG monitoring technologies. Metabolic heat conformation (MHC) offers a promising solution due to its noninvasiveness and reliability. However, progress in MHC technology is hindered by the complexities of multisensor integration and the intricate correlation between MHC and BG. Herein, a wearable BG continuous monitoring device based on metabolic heat integrated sensing (MHIS) is developed, combined with a deep learning network to enable continuous BG detection using MHC principles. The MHIS device integrates a miniaturized circuit and intelligent secondary signal processing, allowing for the simultaneous acquisition and integrated operation of various physiological parameters, including metabolic heat production. By integrating a gate recurrent unit neural network, a model is established to facilitate continuous BG monitoring. The wearable device has a certain accuracy, and when analyzed in comparison with commercial noninvasive glucose meters, the mean absolute relative error meets international standards. The deep learning‐enhanced MHIS system proposed in this work enables noninvasive BG monitoring, paving the way for advancements in personalized healthcare management and offering new opportunities in digital healthcare consultation.

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An explainable echo state network trained from photoplethysmography signals for equine life stage prediction

Byfield,

Miller,

Xie

et al. 2024

Neural Comput & Applic

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Towards Robust Blood Pressure Estimation From Pulse Wave Velocity Measured by Photoplethysmography Sensors

Cited by 19 publications

References 30 publications

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Deep Learning‐Coupled Metabolic Heat Integrated Sensing System for Noninvasive Continuous Monitoring of Blood Glucose

An explainable echo state network trained from photoplethysmography signals for equine life stage prediction

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