Abstract
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.
Highlights
The last decade has seen the development of some wearable technologies for health care [1].These technologies are used for monitoring the saturation of percutaneous oxygen (SpO2 ) [2], the electrocardiogram (ECG) [3], body temperature, physical activities [4], and the respiratory rate.These aforementioned measurement techniques can be combined into a single device that performs a complete physiological monitoring, similar to physiological clothes and polysomnography
We used the data for each subject to make develop their personalized blood pressure models, and the leave-one-out cross validation was used to validate the performance of the multi-dimension regression model and the deep neural network (DNN)
We found that the pulse transit time (PTT) and HR had the highest correlation with blood pressure
Summary
The last decade has seen the development of some wearable technologies for health care [1].These technologies are used for monitoring the saturation of percutaneous oxygen (SpO2 ) [2], the electrocardiogram (ECG) [3], body temperature, physical activities [4], and the respiratory rate.These aforementioned measurement techniques can be combined into a single device that performs a complete physiological monitoring, similar to physiological clothes and polysomnography. The last decade has seen the development of some wearable technologies for health care [1]. These technologies are used for monitoring the saturation of percutaneous oxygen (SpO2 ) [2], the electrocardiogram (ECG) [3], body temperature, physical activities [4], and the respiratory rate. These aforementioned measurement techniques can be combined into a single device that performs a complete physiological monitoring, similar to physiological clothes and polysomnography.
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