Cuff-Less Blood Pressure Measurement Using the Pulse Transit Time - A Comparison to Cuff-Based Oscillometric 24 Hour Blood Pressure Measurement in Children

Hulpke-Wette, Martin; Göhler, Annelie C.; Hofmann, Eveline; Küchler, G

doi:10.1097/01.hjh.0000539170.57269.50

Cited by 3 publications

(2 citation statements)

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“…If this low-cost, cuffless BP monitors can be developed, it is likely that conventional paradigms of BP measurement would be disrupted. Even some special medical conditions can also benefit from this device; for example, it is proven that cuffless BP measurement still shows high accuracy and reliability in arrhythmia patients [ 28 ] and children [ 29 ]. Although the PPG signal is altered in individuals suffering from obesity [ 30 ], it can be optimized based on multiple types of information fusion [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning

Liang

Ding

et al. 2021

International Journal of Hypertension

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Background. Continuous wavelet transform (CWT) based scalogram can be used for photoplethysmography (PPG) signal transformation to classify blood pressure (BP) with deep learning. We aimed to investigate the determinants that can improve the accuracy of BP classification based on PPG and deep learning and establish a better algorithm for the prediction. Methods. The dataset from PhysioNet was accessed to extract raw PPG signals for testing and its corresponding BPs as category labels. The BP category of normal or abnormal followed the criteria of the 2017 American College of Cardiology/American Heart Association (ACC/AHA) Hypertension Guidelines. The PPG signals were transformed into 224 ∗ 224 ∗ 3-pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. The receiver-operating characteristic and loss and accuracy curves were used to evaluate and compare the performance of different methods. Results. Both wavelet type and segment length could affect the accuracy, and Cgau1 wavelet and segment-300 revealed the best performance (accuracy 90%) without obvious overfitting. This method performed better than previously reported MATLAB Morse wavelet transformed scalogram on both of our proposed CNN and CNN-GoogLeNet. Conclusions. We have established a new algorithm with high accuracy to predict BP classification from PPG via matching of CWT type and segment length, which is a promising solution for rapid prediction of BP classification from real-time processing of PPG signal on a wearable device.

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Section: Discussionmentioning

confidence: 99%

Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning

Liang

Ding

et al. 2021

International Journal of Hypertension

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“…In the system, two points are taken, and the time between two measurements is calculated. These measurements include electrocardiography (ECG) [55,56], PPG, phonocardiography (PCG) [57][58][59], ballistocardiography (BCG) [60][61][62][63], seismocardiography (SCG) [64][65][66], impedance cardiography (ICG) [67][68][69][70], use of electrical impedance tomography (EIT) [71], and ultrasonic audio signals [72][73][74][75][76]. Figure 2a shows the normal and high blood pressure of the blood in the vessel.…”

Section: Noninvasive Blood Pressure Measurement Using Nonocclusive Methodsmentioning

confidence: 99%

Towards wearable blood pressure measurement systems from biosignals: a review

Şentürk¹,

Polat²,

Yücedağ³

2019

Turk J Elec Eng & Comp Sci

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Blood pressure is the pressure by the blood to the vein wall. High blood pressure, which is called silent death, is the cause of nearly 13% of mortality all over the world. Blood pressure is not only measured in the medical environment, but the blood pressure measurement is also a need for people in their daily life. Blood pressure estimation systems with low error rates have been developed besides the new technologies and algorithms. Blood pressure measurements are differentiated as invasive blood pressure (IBP) measurement and noninvasive blood pressure (NIBP) measurement methods. Although IBP measurement provides the most accurate results, it cannot be used in daily life because it can only be performed by qualified medical staff with specialized medical equipment. NIBP measurement is based on measuring physiological signals taken from the body and producing results with decision mechanisms. Oscillometric, pulse transit time (PTT), pulse wave velocity, and feature extraction methods are mentioned in the literature as NIBP.In the oscillometric method of the sphygmomanometer, an electrocardiogram is used in PTT methods as a result of the comparison of signals such as electrocardiography, photoplethysmography, ballistocardiography, and seismocardiography.The increase in the human population and worldwide deaths due to the highly elevated blood pressure makes the need for precise measurements and technological devices more clear. Today, wearable technologies and sensors have been frequently used in the health sector. In this review article, the invasive and noninvasive blood pressure methods, including various biosignals, have been investigated and then compared with each other concerning the measurement of comfort and robust estimation.

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Cuffless Blood Pressure Devices

Bradley

Shimbo

Colburn

et al. 2022

American Journal of Hypertension

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Hypertension is associated with more end-organ damage, cardiovascular events, and disability-adjusted life years lost in the United States compared with all other modifiable risk factors. Several guidelines and scientific statements now endorse the use of out-of-office blood pressure (BP) monitoring with ambulatory BP monitoring or home blood pressure monitoring to confirm or exclude hypertension status based on office BP measurement. Current ambulatory or home BP monitoring devices have been reliant on the placement of a BP cuff, typically on the upper arm, to measure BP. There are numerous limitations to this approach. Cuff-based BP may be not well-tolerated for repeated measurements as is utilized with ambulatory BP monitoring. Further, improper technique, including incorrect cuff placement or use of the wrong cuff size, may lead to erroneous readings, affecting diagnosis and management of hypertension. Compared to devices that utilize a cuff, cuffless BP devices may overcome challenges related to technique, tolerability, and overall utility in the outpatient setting. However, cuffless devices have several potential limitations that limit its routine use for the diagnosis and management of hypertension. The review discusses the different approaches for determining BP using various cuffless devices including engineering aspects of cuffless device technologies, validation protocols to test accuracy of cuffless devices, potential barriers to widespread implementation, and future areas of research. This review is intended for the clinicians who utilizes out-of-office BP monitoring for the diagnosis and management of hypertension.

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Cuff-Less Blood Pressure Measurement Using the Pulse Transit Time - A Comparison to Cuff-Based Oscillometric 24 Hour Blood Pressure Measurement in Children

Cited by 3 publications

References 0 publications

Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning

Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning

Towards wearable blood pressure measurement systems from biosignals: a review

Cuffless Blood Pressure Devices

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