Key result
Radar-based non-invasive blood pressure monitoring tracks systolic pressure within ~9 mmHg error.
Why the study?
Standard upper-arm cuff sphygmomanometers only provide static measurements, cannot capture blood pressure variations over time, can be inaccurate, and cause discomfort.
Does a 60 GHz-radar-based approach accurately measure continuous non-invasive blood pressure in human subjects?
Observational (n=55)
Does a 60 GHz-radar-based approach accurately measure continuous non-invasive blood pressure in human subjects?
A novel 60 GHz-radar-based approach for continuous non-invasive blood pressure measurement demonstrates feasibility but requires further optimization to meet established clinical accuracy standards.
Answers are grounded in this paper and cite their sources.
Exceeds accuracy standards in feasibility testing; leaves open radar-based continuous BP monitoring pending optimization and validation.
Blood pressure monitoring is of paramount importance in the assessment of a human's cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations-it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used-together with the calibration parameters of age, gender, height, and weight-as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach's predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further.
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Vysotskaya et al. (2023) conducted an observational in Healthy volunteers (blood pressure assessment) (n=55). 60 GHz-Radar and neural network regression model vs. CNAP continuous non-invasive blood pressure reference device was evaluated on Prediction error for systolic and diastolic blood pressure (Systolic error 9.2±8.3 mmHg, Diastolic error 7.7±5.7 mmHg). The 60 GHz radar-based approach produced a systolic error of 9.2±8.3 mmHg and a diastolic error of 7.7±5.7 mmHg for continuous non-invasive blood pressure monitoring.
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