Key result
A proposed evidential regression method for continuous blood pressure monitoring from PPG signals achieved state-of-the-art performance on the MIMIC II database while estimating prediction uncertainty.
Why the study?
Existing machine learning-based photoplethysmogram blood pressure measuring methods fall behind measurement guidelines and typically provide only point estimates of SBP and DBP.
Does a machine learning method with evidential regression improve continuous blood pressure monitoring accuracy and uncertainty estimation from PPG signals compared to existing methods?
Does a machine learning method with evidential regression improve continuous blood pressure monitoring accuracy and uncertainty estimation from PPG signals compared to existing methods?
A novel machine learning method using evidential regression enables continuous, accurate blood pressure monitoring from PPG signals with reliable uncertainty estimation.
Answers are grounded in this paper and cite their sources.
May support PPG-based continuous BP monitoring with uncertainty estimates; leaves open prospective clinical validation.
Photoplethysmogram (PPG) signal-based blood pressure (BP) estimation is a promising candidate for modern BP measurements, as PPG signals can be easily obtained from wearable devices in a non-invasive manner, allowing quick BP measurement. However, the performance of existing machine learning-based BP measuring methods still fall behind some BP measurement guidelines and most of them provide only point estimates of systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we present a cutting-edge method which is capable of continuously monitoring BP from the PPG signal and satisfies healthcare criteria such as the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) standards. Furthermore, the proposed method provides the reliability of the predicted BP by estimating its uncertainty to help diagnose medical condition based on the model prediction. Experiments on the MIMIC II database verify the state-of-the-art performance of the proposed method under several metrics and its ability to accurately represent uncertainty in prediction.
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Kim et al. (2021) studied Blood pressure estimation. Evidential regression method for continuous BP monitoring from PPG signals vs. Existing machine learning-based BP measuring methods was evaluated on Blood pressure estimation performance and uncertainty representation. A proposed evidential regression method for continuous blood pressure monitoring from PPG signals achieved state-of-the-art performance on the MIMIC II database while estimating prediction uncertainty.
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