Key result
An individual Gaussian Process model for cuffless blood pressure estimation achieved mean absolute errors of 5.1 mmHg for SBP and 4.6 mmHg for DBP, significantly outperforming a generalized model.
Why the study?
Does an individual Gaussian Process regression model improve the accuracy of cuffless blood pressure estimation using PPG compared to a generalized model?
Does an individual Gaussian Process regression model improve the accuracy of cuffless blood pressure estimation using PPG compared to a generalized model?
Answers are grounded in this paper and cite their sources.
Individualized machine learning models using general PPG waveform features and intermittent calibration provide superior accuracy for long-term cuffless blood pressure monitoring compared to generalized population models.
Using the Plethysmograph (PPG) signal to estimate blood pressure (BP) is attractive given the convenience and possibility of continuous measurement. However, due to the personal differences and the insufficiency of data, the dilemma between the accuracy for a small dataset and the robustness as a general method remains. To this end, we scrutinized the whole pipeline from the feature selection to regression model construction based on a one-month experiment with 11 subjects. By constructing the explanatory features consisting of five general PPG waveform features that do not require the identification of dicrotic notch and diastolic peak and the heart rate, three regression models, which are partial least square, local weighted partial least square, and Gaussian Process model, were built to reflect the underlying assumption about the nature of the fitting problem. By comparing the regression models, it can be confirmed that an individual Gaussian Process model attains the best results with 5.1 mmHg and 4.6 mmHg mean absolute error for SBP and DBP and 6.2 mmHg and 5.4 mmHg standard deviation for SBP and DBP. Moreover, the results of the individual models are significantly better than the generalized model built with the data of all subjects.
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Kido et al. (2021) studied Healthy or mixed subjects (implied) (n=11). Individual Gaussian Process Regression model using PPG features vs. Generalized regression model (built with all subjects' data) was evaluated on Mean absolute error (MAE) for systolic and diastolic blood pressure (p=<0.001). An individual Gaussian Process model for cuffless blood pressure estimation achieved mean absolute errors of 5.1 mmHg for SBP and 4.6 mmHg for DBP, significantly outperforming a generalized model.
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