Key result
A five-parameter pulse waveform model reduces systolic BP estimation error by ~16% versus pulse transit time.
Why the study?
Cuffless blood pressure measurement devices based on pulse transit time suffer from inadequate accuracy.
Does a multi-dimension regression model using PPG pulse contour characteristics improve the accuracy of blood pressure estimation compared to using pulse transit time alone?
Does a multi-dimension regression model using PPG pulse contour characteristics improve the accuracy of blood pressure estimation compared to using pulse transit time alone?
Absolute Event Rate: 6.9% vs 8.2%
Using multiple contour characteristics of PPG pulse waves in a regression model improves the accuracy of cuffless blood pressure estimation compared to using pulse transit time alone.
Cuffless PTT-based monitors should not yet replace cuffs in practice; leaves open need for accuracy validation in wearables.
With the advancement of wearable technology, many physiological monitoring instruments are gradually being converted into wearable devices. However, as a consumer product, the blood pressure monitor is still a cuff-type device, which does perform a beat-by-beat continuous blood pressure measurement. Consequently, the cuffless blood pressure measurement device was developed and it is based on the pulse transit time (PTT), although its accuracy remains inadequate. According to the cardiac hemodynamic theorem, blood pressure relates to the arterial characteristics and the contours of the pulse wave include some characteristics of the artery. Therefore, the purpose of this study was to use the contour characteristics of the pulses measured by photoplethysmography (PPG) to estimate the blood pressure using a linear multi-dimension regression model. Ten subjects participated in the experiment, and the blood pressure levels of the subjects were elevated by exercise. The results showed that the mean and standard deviation (mean ± SD) of the root mean square error of the estimated systolic and diastolic pressures within the best five parameters were 6.9 ± 2.81 mmHg and 4.0 ± 0.65 mmHg, respectively. Compared to the results that used one parameter, the PTT, for estimating the systolic and diastolic pressures, 8.2 ± 2.1 mmHg and 4.5 ± 0.79 mmHg, respectively, our results were better.
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Liu et al. (2019) studied Healthy (blood pressure elevated by exercise) (n=10). Multi-dimension regression model using 5 pulse waveform parameters vs. Pulse transit time (PTT) parameter alone was evaluated on Root mean square error (ERMS) of estimated systolic pressure. A linear multi-dimension regression model using five pulse waveform parameters estimated systolic and diastolic blood pressures with root mean square errors of 6.9 mmHg and 4.0 mmHg, respectively, outperforming estimation using pulse transit time alone.
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