A fast digital chip implementing a real-time noise-resistant algorithm for estimating blood pressure using a non-invasive, cuffless PPG sensor

Wang, Jeremy H.S.; Yeh, Ming–Kung; Chao, Paul C.-P.; Tu, Tse Yi; Kao, Yung Hua; Pandey, Rajeev

doi:10.1007/s00542-020-04946-y

Cited by 10 publications

(9 citation statements)

References 23 publications

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“…Several studies agree on considering pulse rate variability assessed from PPG as a surrogate of heart rate variability [ 15 , 81 , 82 ], and recent studies have also investigated the use of PPG time series shorter than 300 samples as an alternative for the standard ST analysis [ 31 , 83 ]. Moreover, analyzing PPG signals also allows information to be extracted on blood pressure [ 84 ], and this would to achieve insight into both heart rate and blood pressure variability which, as seen from our results, often yield complementary results.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures

Volpes

Barà

Busacca

et al. 2022

Sensors

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Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time–domain indices are computed, together with entropy-based measures able to assess the regularity and complexity of cardiovascular dynamics, on time series lasting down to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results are evidence that shorter time series down to 120 samples still exhibit an acceptable agreement with the ST reference and can also be exploited to discriminate between stress and rest. Moreover, despite neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting in its suitability to be implemented on wearable devices.

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Section: Discussionmentioning

confidence: 99%

Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures

Volpes

Barà

Busacca

et al. 2022

Sensors

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show abstract

“…Several studies agree on considering pulse rate variability assessed from PPG as a surrogate of heart rate variability [23,79,80] and recent studies have also investigated the use of PPG time series shorter than 300-samples as an alternative for the standard ST analysis [33,78]. Moreover, analyzing PPG signals allow to extract information also on blood pressure [81,82], and this would permit to achieve an insight into both heart rate and blood pressure variability which, as seen from our results, often yield complementary results.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Ultra-Short Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and During Stress via Time-domain and Entropy-based Measures

Volpes

Barà

Busacca

et al. 2022

Preprint

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Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~5 minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time-domain indices are computed, together with entropy-based measures able to assess regularity and complexity of cardiovascular dynamics, on time series lasting up to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results evidence that shorter time series up to 120 samples still exhibit an acceptable agreement with the ST reference, and can be exploited to dis-criminate between stress and rest as well. Moreover, although neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting suitable to be implemented on wearable devices.

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“…Based on the experimental results obtained by [ 38 ], the elastic modulus of an artery can be represented as [ 13 ]:

where

is the elastic modulus at zero pressure and

is the coefficient depending on the particular vessel. The values of these two parameters are taken as 1428.7 and 0.031, respectively, which are the average values obtained in the study carried out by [ 38 ] for the brachial artery and have been used in a number of studies [ 13 , 39 , 40 ].

is the mean blood pressure and can be derived by:

while

and

are parameters that need to be calibrated for an individual by experiment [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

“…The peak of the reflected wave can be located using the second derivative of the PPG signal (diastolic peak) [ 41 ]. The separation between the systolic and diastolic peaks on the time axis marks the reflective PTT [ 40 ]. Figure 5 shows the PPG waveform, its first and second derivatives, and the R-PTT.…”

Section: Methodsmentioning

confidence: 99%

“…An open-source software, PulseAnalyse [ 42 ], was used in this work to compute the R-PTT values. In previous works, where the reflective pulse transit time was used to estimate blood pressure [ 13 , 39 , 40 ], the R-PTT was utilized in the context of one point-to-point (oPTP) calibration. However, studies that were conducted on PTT-based BP estimation have shown that the oPTP method is not robust [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

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Cuffless Blood Pressure Estimation Using Calibrated Cardiovascular Dynamics in the Photoplethysmogram

Samimi

Dajani

2022

Bioengineering

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An important means for preventing and managing cardiovascular disease is the non-invasive estimation of blood pressure. There is particular interest in developing approaches that provide accurate cuffless and continuous estimation of this important vital sign. This paper proposes a method that uses dynamic changes of the pulse waveform over short time intervals and calibrates the system based on a mathematical model that relates reflective PTT (R-PTT) to blood pressure. An advantage of the method is that it only requires collecting the photoplethysmogram (PPG) using one optical sensor, in addition to initial non-invasive measurements of blood pressure that are used for calibration. This method was applied to data from 30 patients, resulting in a mean error (ME) of 0.59 mmHg, a standard deviation of error (SDE) of 7.07 mmHg, and a mean absolute error (MAE) of 4.92 mmHg for diastolic blood pressure (DBP) and an ME of 2.52 mmHg, an SDE of 12.15 mmHg, and an MAE of 8.89 mmHg for systolic blood pressure (SBP). These results demonstrate the possibility of using the PPG signal for the cuffless continuous estimation of blood pressure based on the analysis of calibrated changes in cardiovascular dynamics, possibly in conjunction with other methods that are currently being researched.

show abstract

A fast digital chip implementing a real-time noise-resistant algorithm for estimating blood pressure using a non-invasive, cuffless PPG sensor

Cited by 10 publications

References 23 publications

Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures

Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures

Feasibility of Ultra-Short Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and During Stress via Time-domain and Entropy-based Measures

Cuffless Blood Pressure Estimation Using Calibrated Cardiovascular Dynamics in the Photoplethysmogram

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