Why the study?
Blood pressure is a key physiological indicator for predicting and diagnosing hypertension and cardiovascular diseases, motivating continuous BP estimation from pulse waves.
Does a two-domain fusion model using pulse wave data accurately estimate continuous blood pressure compared to a commercial sphygmomanometer?
Does a two-domain fusion model using pulse wave data accurately estimate continuous blood pressure compared to a commercial sphygmomanometer?
A novel two-domain fusion model using neural networks can accurately estimate continuous blood pressure from radial artery pulse waves.
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May support wearable continuous BP monitoring; leaves open clinical validation before practice adoption.
Blood pressure (BP) is one of the indispensable elements of physiological health characteristics and a significant indicator for predicting and diagnosing hypertension and cardiovascular diseases. This paper proposes a two-domain fusion model to estimate BP continuously from pulse wave acquired with a pressure sensor. Method . The optimal external pressure applied on the pressure sensor is first determined in order to capture pulse wave in the radial artery. The captured pulse wave is then processed in both the time and frequency domains via filtering and fast Fourier transform. Finally, a set of features are extracted from these two domains and input into a neural network along with blood pressure values measured by a commercial sphygmomanometer for training. The model is then tested on new data for accuracy evaluation. Results . The proposed two-domain fusion method achieved a high degree of accuracy in measuring blood pressure.
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Wang et al. (2018) studied this question.
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