Machine learning based, subject-specific, gender and race independent, non-invasive estimation of the arterial blood pressure.

Software-based blood pressure (BP) measurement offers non-invasive, continuous, real-time monitoring compared to traditional methods. This study explores a non-invasive machine learning approach to estimate arterial BP from ECG and SpO2 signals, using intra-arterial catheter BP readings as ground truth. A random forest (RF) algorithm was trained on a large dataset (~30 M beats, ~400 patient days), using extracted signal morphological features and patient characteristics. The RF model achieved low mean absolute error (MAE) for systolic/diastolic BP (4.29 ± 5.00 mmHg/2.38 ± 3.25 mmHg), independent of gender and race. Personalized models further improved performance (MAE: 3.51 ± 4.24 mmHg/1.85 ± 2.60 mmHg). We assessed different ECG lead combinations for estimating BP and observed that two limb leads, or a precordial lead were sufficient for an estimation below 5 mmHg MAE. These findings highlight the potential of real-time, personalized BP monitoring for early detection of hypertension, enhancing healthcare accessibility through non-invasive, continuous monitoring.