Abstract
We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = −0.3 ± 5.8 mmHg; SVR and −0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = −1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity.
Highlights
Blood pressure, commonly expressed in terms of systolic and diastolic pressures, offers important insights into cardiovascular health
Three different blood pressure estimation models were evaluated in the present study, including the conventional Maximum Amplitude Algorithm (MAA) method based on fixed characteristic ratios, and two newly proposed models were obtained using multiple linear regression (MLR) and support vector regression (SVR)
We further demonstrated from our analysis results (Tables 5 and 6) that the usage of variable characteristic ratio derived based on several features extracted from the oscillometric waveform envelope (OWE) improved the blood pressure estimation accuracy over the conventional MAA method using fixed characteristic ratios (SBP: mean ± standard deviation (SD) = −1.6 ± 8.6 mmHg; diastolic blood pressure (DBP): mean ± SD = 0.3 ± 6.7 mmHg)
Summary
Commonly expressed in terms of systolic (maximum) and diastolic (minimum) pressures, offers important insights into cardiovascular health. The main drawback of the slope-based method is that it defines SBP and DBP as the cuff pressure corresponding to the maximum slope of increasing and decreasing amplitude of the OWE, which are not well defined and constraints have to be applied to estimate SBP with an acceptable accuracy [8]. Feature-based Gaussian mixture regression approach [15] as well as neural network [16], Bayesian model [7], and a statistical learning technique based on logistic regression [17] were among the alternative methods Five features, such as MAP, maximum amplitude, length of the maximum amplitude’s position, length of OWE and asymmetry ratio of the OWE were used to estimate SBP and DBP using the Gaussian mixture regression model [15].
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