Key result
Causal inference-based cuffless BP model beats traditional methods with ~5 mmHg SBP estimation error.
Why the study?
Data-driven cuffless BP estimation models have largely overlooked causality, motivating this examination of whether causal inference is feasible for cuffless BP estimation.
Does a causal inference-based model improve the accuracy of cuffless blood pressure estimation compared to traditional models?
Does a causal inference-based model improve the accuracy of cuffless blood pressure estimation compared to traditional models?
Integrating causal inference into cuffless blood pressure estimation models using ECG and PPG signals improves estimation accuracy compared to traditional correlation-based models.
Answers are grounded in this paper and cite their sources.
Requires prospective validation before clinical use; leaves open causal modeling for cuffless BP in larger trials.
Enabled by wearable sensing, e.g., photoplethysmography (PPG) and electrocardiography (ECG), and machine learning techniques, study on cuffless blood pressure (BP) measurement with data-driven methods has become popular in recent years. However, causality has been overlooked in most of current studies. In this study, we aim to examine the feasibility of causal inference for cuffless BP estimation. We first attempt to detect wearable features that are causally related, rather than correlated, to BP changes by identifying causal graphs of interested variables with fast causal inference (FCI) algorithm. With identified causal features, we then employ time-lagged link to integrate the mechanism of causal inference into the BP estimated model. The proposed method was validated on 62 subjects with their continuous ECG, PPG and BP signals being collected. We found new causal features that can better track BP changes than pulse transit time (PTT). Further, the developed causal-based estimation model achieved an estimation error of mean absolute difference (MAD) being 5.10 mmHg and 2.85 mmHg for SBP and DBP, respectively, which outperformed traditional model without consideration of causality. To the best of our knowledge, this work is the first to study the causal inference for cuffless BP estimation, which can shed light on the mechanism, method and application of cuffless BP measurement.
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Liu et al. (2023) studied Blood pressure measurement (n=62). Causal inference-based estimation model vs. Traditional model without consideration of causality was evaluated on Estimation error of mean absolute difference (MAD) for SBP and DBP. A causal inference-based model for cuffless blood pressure estimation achieved a mean absolute difference of 5.10 mmHg for SBP and 2.85 mmHg for DBP, outperforming traditional models.
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