Approximately a quarter of the world’s population is affected by high blood pressure (BP). It is a major risk factor for stroke and heart disease, which are leading causes of mortality. Management of hypertension could be improved by increased accuracy and convenience of BP measurement devices. Existing devices are not convenient or portable enough. In this work, we investigate three approaches to improve the accuracy and convenience of BP measurement. A physiologic method was developed to further advance central BP measurement. A patient-specific method was applied to estimate brachial BP levels from a cuff pressure waveform obtained during conventional deflation via a nonlinear arterial compliance model. A physiologically-inspired method was then employed to extract the PVP waveform from the same waveform via ensemble averaging and calibrate it to the brachial BP levels. A method based on a wave reflection model was thereafter employed to define a variable transfer function, which was applied to the calibrated waveform to derive central BP. This method was evaluated against invasive central BP measurements from patients. The method yielded central systolic, diastolic, and pulse pressure bias and precision errors of −0.6 to 2.6 and 6.8 to 9.0 mmHg. The conventional oscillometric method produced similar bias errors but precision errors of 8.2 to 12.5 mmHg (p ≤ 0.01). The new method can derive central BP more reliably than some current non-invasive devices and in the same way as traditional cuff BP. We then developed an iPhone X application to measure cuff-less BP via the “oscillometric finger pressing method”. The user presses her fingertip on both the front camera and screen to increase the external pressure of the underlying artery, while the application measures the resulting variable-amplitude blood volume oscillations via the camera and applied pressure via the strain gauge array under the screen. The application also visually guides the fingertip placement and actuation and then computes BP from the measurements just like many automatic cuff devices. We tested the application, along with a finger cuff device, against a standard cuff device. The application yielded bias and precision errors of −4.0 and 11.4 mmHg for systolic BP and −9.4 and 9.7 mmHg for diastolic BP (n = 18). These errors were near the finger cuff device errors. This proof-of-concept study surprisingly indicates that cuff-less and calibration-free BP monitoring may be feasible with many existing and forthcoming smartphones. Finally, we developed easy-to-understand models relating PPG waveform features to BP changes (after a single cuff calibration) and determined conclusively whether they provide added value or not in BP measurement accuracy. Stepwise linear regression was employed so as to create parsimonious models for predicting the intervention-induced BP changes from popular PPG waveform features, pulse arrival time (PAT, time delay between ECG R-wave and PPG foot), and subject demographics. The finger b-time (PPG foot to minimum second derivative time) and ear STT (PPG amplitude divided by maximum derivative), when combined with PAT, reduced the systolic BP change prediction RMSE of reference models by 6-7% (p<0.022). The ear STT together with the pulse width reduced the diastolic BP change prediction RMSE of the reference model by 13% (p=0.003). Hence, PPG fast upstroke time intervals can offer some added value in cuff-less measurement of BP changes.
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