Continuous blood pressure prediction from pulse transit time using ECG and PPG signals

Ghosh, Shrimanti; Banerjee, Ankur; Ray, Nilanjan; Wood, Peter W.; Boulanger, Pierre; Padwal, Raj

doi:10.1109/hic.2016.7797728

Cited by 56 publications

(22 citation statements)

References 7 publications

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“…HR and blood pressure are closely correlated, and hypertensive patients have higher resting HR than normotensive patients [33]. Blood pressure can be measured continuously based on ECG and S p O 2 [34] or based on ECG and PPG signals [23]. PPG, aVF and S p O 2 are common important physiological parameters that affect human blood pressure.…”

Section: Methodsmentioning

confidence: 99%

“…Cardiovascular reactivity and recovery are laboratory stress-induced cardiovascular changes that can be used to predict trends in blood pressure and HR over the next three to ten years. Ghosh et al [23] combined ECG with PPG characteristic data signals and calculated the pulse wave transmission time (PTT), which has been used in LinearR models to predict blood pressure. Peng et al [24] used the heartbeat signal characteristics to establish a support vector machine (SVR) regression model for continuous and cuffless blood pressure measurement, but the predictive accuracy was not high.…”

Section: Introductionmentioning

confidence: 99%

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Predicting blood pressure from physiological index data using the SVR algorithm

et al. 2019

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Background Blood pressure diseases have increasingly been identified as among the main factors threatening human health. How to accurately and conveniently measure blood pressure is the key to the implementation of effective prevention and control measures for blood pressure diseases. Traditional blood pressure measurement methods exhibit many inherent disadvantages, for example, the time needed for each measurement is difficult to determine, continuous measurement causes discomfort, and the measurement process is relatively cumbersome. Wearable devices that enable continuous measurement of blood pressure provide new opportunities and hopes. Although machine learning methods for blood pressure prediction have been studied, the accuracy of the results does not satisfy the needs of practical applications. Results This paper proposes an efficient blood pressure prediction method based on the support vector machine regression (SVR) algorithm to solve the key gap between the need for continuous measurement for prophylaxis and the lack of an effective method for continuous measurement. The results of the algorithm were compared with those obtained from two classical machine learning algorithms, i.e., linear regression (LinearR), back propagation neural network (BP), with respect to six evaluation indexes (accuracy, pass rate, mean absolute percentage error (MAPE), mean absolute error (MAE), R-squared coefficient of determination ( R 2 ) and Spearman’s rank correlation coefficient). The experimental results showed that the SVR model can accurately and effectively predict blood pressure. Conclusion The multi-feature joint training and predicting techniques in machine learning can potentially complement and greatly improve the accuracy of traditional blood pressure measurement, resulting in better disease classification and more accurate clinical judgements.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting blood pressure from physiological index data using the SVR algorithm

et al. 2019

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“…Previous cuff-less and PTT-based studies are typically classified into three categories [4], [10]: 1) adopting only an electrocardiogram (ECG) signal [26], [27], 2) adopting both ECG and PPG signals [28]- [32], and 3) adopting only a PPG signal [22], [23], [33]. The research adopting the approaches of 1) and 2) can be difficult to apply in daily life, since the ECG signal is derived from sensors attached to the body.…”

Section: Introductionmentioning

confidence: 99%

Cuff-Less Blood Pressure Monitoring System Using Smartphones

et al. 2020

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Recently, smartphones with mobile health applications have become promising tools in the healthcare industry due to their convenience, ubiquity for patients, and the ability to gather data in real time. In this paper, we propose a novel non-invasive, portable, and cuff-less method for monitoring BP by only using the smartphones' camera. Our experiment uses pulse transit time (PTT) between two separate photoplethysmogram (PPG) signals to estimate the subjects' systolic blood pressure (SBP) and diastolic blood pressure (DBP). Our proposed method first measures the subject's PPG signals from his/her index fingers using the smartphones' camera. Then, filtering and peak detection algorithms of the proposed method reduce the motion and noise artifacts in the PPG signals. Finally, the proposed method estimates SBP and DBP based on a linear regression model which was trained and tested on 30 trials with six healthy subjects. We evaluated the proposed method by comparing BP values of the proposed method with those of the reference (or gold-standard) device in terms of mean absolute error (MAE), standard deviation of error (SD), and R-squared (R 2) value of the cross-validation. Experimental results show that the proposed method estimates the average of MAE ± SD is 2.07 ± 2.06 mm Hg for SBP estimation, and 2.12 ± 1.85 mm Hg for DBP estimation. These estimates are lower than accurate BP estimation standard (5 ± 8 mmHg). INDEX TERMS Hypertension, blood pressure, cuff-less, smartphone, photoplethysmogram (PPG), pulse transit time (PTT).

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“…Even if the number of inflations during a 24h measurement are perceived to be quite frequent, the resulting BP time series has a very low time resolution. Continuous beat-to-beat measurements of BP obtained by the computation of the PTT from electrocardiography (ECG) and peripheral PPG signals however, are disputable for several reasons: (i) the dependency on the location and the distance of measurement points, (ii) the method used to find and interpret minima, maxima and saddle points within the PPG and ECG signals [5,6], (iii) the appearance of undetermined fluctuations in the time between the pressure wave and the ECG R-peak during blood ejection of the left ventricle [7] and (iv) the influences of auto-regulation on arterial stiffness that produce undetermined drifts of the PTT over time [8]. Nonetheless, the PTT method is subject of several publications that aim at a development of wearable continuous BPM devices [5,[9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…The general relationship between BP and PTT was subject of a series of investigations. In [6,[14][15][16][17][18][19][20][21][22] different magnitudes for the computation of the PTT are based on the calculation of the time difference between the ECG R-peak and some reference within the PPG signal and are methodically described and validated by calculating a correlation to the IBP. In addition to the methodological aspects of the BP computation using the PTT, [23] also discusses the impact of influencing factors, like changes in individual arterial stiffness.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Quasi-Continuous Blood Pressure based on Harmonic Phase-Shifts in Signals using Non-Invasive Photoplethysmographic Measurements

Kern

Bernhard

2019

Preprint

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According to the guidelines of the European Society of Hypertension International Protocol revision 2010, the requirements for long time blood pressure measurement (BPM) are: simple handling, robustness against movements, accuracy of better than 5 mmHg, and, above all, that the patient's motion should not be restricted during measurement. These requirements are in particular important for a reliable interpretation of the blood pressure (BP) of hypertensive patients, because such a diagnosis will usually be confirmed by long-term measurement. Moreover, to be able to correlate the patient's BP with his normal daily activity, non-obstructive non-invasive methods are desired to reduce the patient's load. The main concern of this paper is to present a novel method for estimating non-invasive continuous blood pressure (CBP) from a single photoplethysmography (PPG) signal. In contrast to the pulse transit time (PTT) method, our approach is based on the assumption that the phase-velocities of the fundamental and higher harmonics depend on the (non-linear) elastic properties of the arteries. Consequently, phase velocity varies as a function of a vessel's instantaneous dilation and can be effectively utilised for CBP estimation. In addition to its numerous advantages for a simplified measurement setup, we could show that the method achieves a high degree of correlation for a reliable BP estimation from PPG data. Comparison with state-of-the-art PTT methods was carried out using a dataset from the PhysioBank Database comprising a reference invasive blood pressure (IBP) signal measured at the radial artery, a PPG signal measured at the fingertip and a standard ECG signal. The correlation values obtained from the long-time estimation of the systolic blood pressure (SBP) were as high as r = 0.8945, while the value for the diastolic blood pressure (DBP) was found to be r = 0.9082 and the correlation of the mean blood pressure (MBP) was r = 0.9322. These results were achieved by analysing the dataset in a beat-to-beat manner and regarding several post-processing procedures like coherent averaging (CA) and zero padding with quasi-continuous frequency domain estimation and artificially refined frequency resolution.

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Continuous blood pressure prediction from pulse transit time using ECG and PPG signals

Cited by 56 publications

References 7 publications

Predicting blood pressure from physiological index data using the SVR algorithm

Predicting blood pressure from physiological index data using the SVR algorithm

Cuff-Less Blood Pressure Monitoring System Using Smartphones

Estimation of Quasi-Continuous Blood Pressure based on Harmonic Phase-Shifts in Signals using Non-Invasive Photoplethysmographic Measurements

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