Abstract
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.
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Details
1 Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA (ROR: https://ror.org/002pd6e78) (GRID: grid.32224.35) (ISNI: 0000 0004 0386 9924)
2 Anesthesia Department, Massachusetts General Hospital, Boston, MA, USA (ROR: https://ror.org/002pd6e78) (GRID: grid.32224.35) (ISNI: 0000 0004 0386 9924)
3 Healthcare Transformation Lab, Massachusetts General Hospital, Boston, MA, USA (ROR: https://ror.org/002pd6e78) (GRID: grid.32224.35) (ISNI: 0000 0004 0386 9924)
4 Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA (ROR: https://ror.org/002pd6e78) (GRID: grid.32224.35) (ISNI: 0000 0004 0386 9924); Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA (ROR: https://ror.org/042nb2s44) (GRID: grid.116068.8) (ISNI: 0000 0001 2341 2786)