Key result
Causal inference-based features from wearable ECG and PPG signals detected hypertension with 89% accuracy, outperforming correlation-based features.
Why the study?
Most wearable ECG and PPG hypertension prediction relies on correlation-based features, which can be unreliable and confounded, prompting exploration of causal inference methods.
Does a causal inference-based approach using wearable ECG and PPG signals improve the accuracy of hypertension detection compared to correlation-based features?
Observational (n=405)
Does a causal inference-based approach using wearable ECG and PPG signals improve the accuracy of hypertension detection compared to correlation-based features?
A causal inference-based approach using wearable ECG and PPG signals can effectively detect hypertension and outperforms traditional correlation-based feature extraction.
Answers are grounded in this paper and cite their sources.
Causal features from wearables may aid hypertension detection; leaves open clinical utility and generalizability pending prospective trials.
Background: Hypertension is a leading cause of cardiovascular disease and premature death worldwide, and it puts a heavy burden on the healthcare system. Therefore, it is very important to detect and evaluate hypertension and related cardiovascular events to enable early prevention, detection, and management. Hypertension can be detected in a timely manner with cardiac signals, such as through an electrocardiogram (ECG) and photoplethysmogram (PPG) , which can be observed via wearable sensors. Most previous studies predicted hypertension from ECG and PPG signals with extracted features that are correlated with hypertension. However, correlation is sometimes unreliable and may be affected by confounding factors . Objective: The aim of this study was to investigate the feasibility of predicting the risk of hypertension by exploring features that are causally related to hypertension via causal inference methods. Additionally, we paid special attention to and verified the reliability and effectiveness of causality compared to correlation. Methods: We used a large public dataset from the Aurora Project , which was conducted by Microsoft Research. The dataset included diverse individuals who were balanced in terms of gender, age, and the condition of hypertension, with their ECG and PPG signals simultaneously acquired with wrist -worn wearable devices. We first extracted 205 features from the ECG and PPG signals, calculated 6 statistical metrics for these 205 features, and selected some valuable features out of the 205 features under each statistical metric. Then, 6 causal graphs of the selected features for each kind of statistical metric and hypertension were constructed with the equivalent greedy search algorithm. We further fused the 6 causal graphs into 1 causal graph and identified features that were causally related to hypertension from the causal graph . Finally, we used these features to detect hypertension via machine learning algorithms. Results: We validated the proposed method on 405 subjects. We identified 24 causal features that were associated with hypertension. The causal features could detect hypertension with an accuracy of 89%, precision of 92 % , and recall of 82%, which outperformed detection with correlation features (accuracy of 85%, precision of 88 % , and recall of 77%). Conclusions: The results indicated that the causal inference -based approach can potentially clarify the mechanism of hypertension detection with noninvasive signals and effectively detect hypertension. It also reveal ed that causality can be more reliable and effective than correlation for hypertension detection and other application scenarios.
No takes yet. Share an insight, caveat, or question.
G et al. (2024) conducted an observational in Hypertension (n=405). Causal inference-based features from ECG and PPG signals vs. Correlation-based features was evaluated on Hypertension detection accuracy. Causal inference-based features from wearable ECG and PPG signals detected hypertension with 89% accuracy, outperforming correlation-based features.
Synapse has enriched 10 related papers. Consider them for comparative context: