Why the study?
ECG and PPG signals used for cuffless blood pressure estimation are often corrupted by noise and artefacts, leading to distorted BP estimates.
Does a probabilistic beat-to-beat filtering model improve the accuracy of continuous blood pressure estimation from ECG and PPG signals?
Does a probabilistic beat-to-beat filtering model improve the accuracy of continuous blood pressure estimation from ECG and PPG signals?
Answers are grounded in this paper and cite their sources.
A novel probabilistic filtering model improves the accuracy of continuous, cuffless blood pressure estimation from ECG and PPG signals by effectively handling measurement noise and artifacts.
Cuffless technologies provide a convenient platform for remote and continuous blood pressure (BP) monitoring, however, signal recordings employed for cuffless BP estimation, which are based on the electrocardiogram (ECG) and photo-plethysmography (PPG) signals, are frequently corrupted with measurement noise and artefacts. Consequently, even a small portion of abnormal data samples can severely impact the overall signal quality and therefore lead to significantly distorted BP value estimates. To this end, a data-driven model is proposed to infer the beat-to-beat signal quality for the ECG, PPG and BP signal recordings, whereby high-quality and low-quality (outlier) beats are detected using a probabilistic model chosen according to the maximum entropy principle. Physiological rules are also imposed to guarantee that each filtered sample is physiologically meaningful. The advantages of the proposed filtering framework for both systolic blood pressure and diastolic blood pressure estimation are demonstrated through the analysis and estimation of 12,000 clinical BP recordings, consisting of over 200,000 test samples.
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Chen et al. (2020) studied this question.
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