Key result
Machine learning-enabled NIBP sensors achieve promising accuracy for continuous blood pressure monitoring.
Why the study?
Traditional cuff-based blood pressure monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements in conscious individuals.
Do non-invasive blood pressure sensors combined with machine learning models accurately predict continuous blood pressure compared to traditional cuff methods?
Do non-invasive blood pressure sensors combined with machine learning models accurately predict continuous blood pressure compared to traditional cuff methods?
Emerging cuffless NIBP monitoring technologies powered by machine learning offer the potential for continuous, real-time cardiovascular assessment, though challenges in artifact reduction and calibration remain.
May support continuous cuffless BP monitoring; leaves open need for prospective validation before clinical adoption.
Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.
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Ismail et al. (2022) conducted a review in Hypertension / Need for blood pressure monitoring. Non-invasive blood pressure (NIBP) monitoring sensors and machine learning models vs. Traditional cuff sphygmomanometer or invasive arterial catheterization was evaluated on Blood pressure prediction accuracy based on AAMI and BHS standards. Machine learning algorithms combined with NIBP sensors demonstrate promising accuracy for continuous blood pressure monitoring, with some deep learning models meeting international validation standards.
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