Abstract
OBJECTIVE: Oscillometric finger pressing is a potential method for smartphone-based blood pressure (BP) monitoring. A photoplethysmography (PPG)-force sensor unit measures the slowly increasing finger pressure applied by the user under visual guidance and the resulting variable blood volume oscillations ("AC PPG"). BP can then be estimated from the oscillation height versus finger pressure function. The non-oscillating component of PPG ("DC PPG") during oscillometric finger pressing was investigated. METHODS: The total (AC+DC) PPG waveform, finger pressure, and ECG waveform during finger pressing were measured with a modified custom system along with arm cuff BP in volunteers. A mathematical model accounting for the arterial compliance curve and tissue compression was developed to explain the measured total PPG waveform versus finger pressure function. The model predicted that DC PPG (average of the total PPG over each heartbeat) versus finger pressure function will show a fiducial marker (bend) near finger systolic BP (SP). An algorithm was developed to detect this bend and estimate arm SP from the finger pressing measurements. RESULTS: The model explained the measured total PPG waveform versus finger pressure function over the finger pressure range that is relevant to BP estimation. The model-based algorithm yielded a correlation coefficient of 0.90 and a precision error of 9.2 mmHg against cuff SP (N = 18). CONCLUSION: An easy-to-understand model can explain the total PPG waveform during finger pressing, and SP can be estimated from the DC (non-oscillating) PPG versus finger pressure function. SIGNIFICANCE: These findings may prove useful in converting ubiquitous smartphones into BP sensors.