Cuffless blood pressure estimation from the carotid pulse arrival time using continuous wave radar

Buxi, Dilpreet; Redouté, Jean-Michel; Yuce, Mehmet Rasit

doi:10.1109/embc.2015.7319687

Cited by 20 publications

(16 citation statements)

References 17 publications

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“…Most importantly, this study further analysed the estimation accuracy of the three machine learning algorithms under different BP categories (normotensive, hypertensive, and hypotensive) and found that most of the algorithms exhibited better accuracy in the normotensive category. Previous research only presented overall BP accuracies (overall mean of difference ± SD of difference) rather than individual categorical BP accuracies [ 3 , 9 , 36 ]. Some studies only included normotensive subjects [ 10 , 17 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…MLR is a type of the machine learning algorithm that has been widely used by previous researchers to estimate cuffless BP [ 3 , 7 , 26 ]. The algorithm started with the random selection of coefficients of the linear algorithm ( θ 0 , θ 1 , θ 2 , and θ 3 ).…”

Section: Methodsmentioning

confidence: 99%

“…The key measuring principle for cuffless BP estimation is based upon the time taken by a pulse from the heart to the finger. They are known as pulse transit time (PTT) or pulse arrival time (PAT) [ 3 – 10 ]. Other researchers used vascular transit time (VTT) which was calculated from the time difference between photoplethysmograph (PPG) measured at the fingertip and phonocardiograph measured at the chest [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches

Khalid

Zhang

Chen

et al. 2018

Journal of Healthcare Engineering

143

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Introduction Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the most useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement mainly relies on cuff-based techniques that cause inconvenience and discomfort to users. Although some of the present prototype cuffless BP measurement techniques are able to reach overall acceptable accuracies, they require an electrocardiogram (ECG) and a photoplethysmograph (PPG) that make them unsuitable for true wearable applications. Therefore, developing a single PPG-based cuffless BP estimation algorithm with enough accuracy would be clinically and practically useful. Methods The University of Queensland vital sign dataset (online database) was accessed to extract raw PPG signals and its corresponding reference BPs (systolic BP and diastolic BP). The online database consisted of PPG waveforms of 32 cases from whom 8133 (good quality) signal segments (5 s for each) were extracted, preprocessed, and normalised in both width and amplitude. Three most significant pulse features (pulse area, pulse rising time, and width 25%) with their corresponding reference BPs were used to train and test three machine learning algorithms (regression tree, multiple linear regression (MLR), and support vector machine (SVM)). A 10-fold cross-validation was applied to obtain overall BP estimation accuracy, separately for the three machine learning algorithms. Their estimation accuracies were further analysed separately for three clinical BP categories (normotensive, hypertensive, and hypotensive). Finally, they were compared with the ISO standard for noninvasive BP device validation (average difference no greater than 5 mmHg and SD no greater than 8 mmHg). Results In terms of overall estimation accuracy, the regression tree achieved the best overall accuracy for SBP (mean and SD of difference: −0.1 ± 6.5 mmHg) and DBP (mean and SD of difference: −0.6 ± 5.2 mmHg). MLR and SVM achieved the overall mean difference less than 5 mmHg for both SBP and DBP, but their SD of difference was >8 mmHg. Regarding the estimation accuracy in each BP categories, only the regression tree achieved acceptable ISO standard for SBP (−1.1 ± 5.7 mmHg) and DBP (−0.03 ± 5.6 mmHg) in the normotensive category. MLR and SVM did not achieve acceptable accuracies in any BP categories. Conclusion This study developed and compared three machine learning algorithms to estimate BPs using PPG only and revealed that the regression tree algorithm was the best approach with overall acceptable accuracy to ISO standard for BP device validation. Furthermore, this study demonstrated that the regression tree algorithm achieved acceptable measurement accuracy only in the normotensive category, suggesting that future algorithm development for BP estimation should be more specific for different BP categories.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches

Khalid

Zhang

Chen

et al. 2018

Journal of Healthcare Engineering

143

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“…The frequency of human breathing can range anywhere from 0.1 to 0.5 Hz [9]. Data in the receiver will be a phase-shifted signal, which will include respiration information [10]. A field-programmable gate array (FPGA)-based system was designed and implemented to detect human heartbeat and respiration using Doppler processing.…”

Section: Introductionmentioning

confidence: 99%

“…The systems developed by [2,10,11,15,16,17] were used to measure vital signs, but only in lying or seated positions. The experiments in [4,19,20,21] were done in standing and walking positions; however, only one subject took part in each test.…”

Section: Introductionmentioning

confidence: 99%

A Doppler Radar System for Sensing Physiological Parameters in Walking and Standing Positions

Ebrahim

Sarvi

Yuce

2017

Sensors

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Doppler radar can be implemented for sensing physiological parameters wirelessly at a distance. Detecting respiration rate, an important human body parameter, is essential in a range of applications like emergency and military healthcare environments, and Doppler radar records actual chest motion. One challenge in using Doppler radar is being able to monitor several patients simultaneously and in different situations like standing, walking, or lying. This paper presents a complete transmitter-receiver Doppler radar system, which uses a 4 GHz continuous wave radar signal transmission and receiving system, to extract base-band data from a phase-shifted signal. This work reports experimental evaluations of the system for one and two subjects in various standing and walking positions. It provides a detailed signal analysis of various breathing rates of these two subjects simultaneously. These results will be useful in future medical monitoring applications.

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Contactless and continuous blood pressure measurement according to caPTT obtained from millimeter wave radar

Geng,

Bai,

Zhang

et al. 2023

Measurement

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Cuffless blood pressure estimation from the carotid pulse arrival time using continuous wave radar

Cited by 20 publications

References 17 publications

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches

A Doppler Radar System for Sensing Physiological Parameters in Walking and Standing Positions

Contactless and continuous blood pressure measurement according to caPTT obtained from millimeter wave radar

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