OBJECTIVE: Our objective is to create a blood pressure measurement device, which may provide a way to easily acquire frequent measurements. Common techniques to measure blood pressure include an arterial catheter, an oscillometric pressure cuff, or an auscultatory pressure cuff. METHODS: The approach takes as input ultrasound images of an artery and contact force between the ultrasound array and subject. A subject may perform the self-measurements. Image and force data is analyzed for its quality and used to provide guidance or reject poor measurements. Tissue motions, due to probe contact forces and pulsing blood pressure, are estimated from the ultrasound image. Tissues elasticities and blood pressure are found by optimally fitting the observed tissue motion versus applied forces to a table of predicted motion-pre-generated with a finite element tissue deformation model. The output of the optimization is an estimate of systolic and diastolic blood pressure, arterial stiffness, and surrounding tissue stiffness. RESULTS: The real-time implementation of the algorithm was validated on a cohort of 21 single-visit volunteers and on four volunteers self-monitored longitudinally. The systolic and diastolic pressures were compared to oscillometric cuff readings. Regression and Bland-Altman analyses were performed. CONCLUSION: Systolic pressure and diastolic pressure can be estimated in real-time and by the subject using this novel non-invasive ultrasound-based method (systolic accuracy/precision: -5.2 mmHg/10.7 mmHg; diastolic accuracy/precision: -3.9/8.0 mmHg). SIGNIFICANCE: The method occupies a middle ground between the arterial catheter and cuff-based techniques. It has the potential to give calibration-free results.
OBJECTIVE: Our objective is to create a blood pressure measurement device, which may provide a way to easily acquire frequent measurements. Common techniques to measure blood pressure include an arterial catheter, an oscillometric pressure cuff, or an auscultatory pressure cuff. METHODS: The approach takes as input ultrasound images of an artery and contact force between the ultrasound array and subject. A subject may perform the self-measurements. Image and force data is analyzed for its quality and used to provide guidance or reject poor measurements. Tissue motions, due to probe contact forces and pulsing blood pressure, are estimated from the ultrasound image. Tissues elasticities and blood pressure are found by optimally fitting the observed tissue motion versus applied forces to a table of predicted motion-pre-generated with a finite element tissue deformation model. The output of the optimization is an estimate of systolic and diastolic blood pressure, arterial stiffness, and surrounding tissue stiffness. RESULTS: The real-time implementation of the algorithm was validated on a cohort of 21 single-visit volunteers and on four volunteers self-monitored longitudinally. The systolic and diastolic pressures were compared to oscillometric cuff readings. Regression and Bland-Altman analyses were performed. CONCLUSION: Systolic pressure and diastolic pressure can be estimated in real-time and by the subject using this novel non-invasive ultrasound-based method (systolic accuracy/precision: -5.2 mmHg/10.7 mmHg; diastolic accuracy/precision: -3.9/8.0 mmHg). SIGNIFICANCE: The method occupies a middle ground between the arterial catheter and cuff-based techniques. It has the potential to give calibration-free results.