Key result
Hybrid neural network outperforms state-of-the-art techniques for BP estimation with a 0.08 mmHg systolic error.
Why the study?
Current oscillometric BP devices vary in accuracy across settings and patient groups because their algorithms cannot fully capture the BP information encoded in oscillometric pulse patterns.
Does a hybrid deep morpho-temporal framework improve the accuracy of oscillometric blood pressure measurement compared to state-of-the-art techniques?
Does a hybrid deep morpho-temporal framework improve the accuracy of oscillometric blood pressure measurement compared to state-of-the-art techniques?
A novel deep learning framework for oscillometric BP measurement significantly improves estimation accuracy, satisfying international standards by a wide margin.
Should not change oscillometric BP practice yet; leaves open prospective clinical validation.
Oscillometric blood pressure (BP) measurement devices are widely utilized as the primary automated BP measurement tools in non-specialist environments. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. An essential constraint of current oscillometric BP measurement devices is their analysis algorithms' incapacity to capture the BP information encoded in the pattern of recorded oscillometric pulses to its fullest extent. In this article, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.
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Delfan et al. (2023) studied Blood pressure measurement (n=245). Hybrid convolutional-recurrent neural network for 2D oscillometric data representation vs. State-of-the-art techniques was evaluated on Estimation of systolic and diastolic blood pressure. A hybrid convolutional-recurrent neural network achieved a mean error of 0.08 mmHg (SD 2.4) for systolic BP and 0.04 mmHg (SD 2.2) for diastolic BP, outperforming state-of-the-art techniques.
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