O19 CAUSAL INFERENCE FOR HYPERTENSION DETECTION WITH WEARABLE DEVICES

Background and Objective: Hypertension is a leading cause of cardiovascular disease and premature death worldwide and it puts a heavy burden on the healthcare system. It is, therefore, very important to detect and evaluate hypertension and related cardiovascular events so as for early prevention, detection and management. Hypertension can be evaluated in real time with wearable noninvasive cardiac signals, such as electro-cardiogram (ECG) and photoplethysmography (PPG). 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. In this study, we explored the feasibility of predicting the risk of hypertension using causal inference methods. Additionally, we paid special attention to and verified the reliability and effectiveness of causality compared to correlation. Methods: Firstly, we constructed causal graphs by the Greedy Equivalence Search algorithm, and then applied causal strategies to obtain the optimal causal graph. Finally, we used machine learning classification algorithms to achieve hypertension prediction. Results: The machine learning classification models achieve great classification performance, with accuracy being 0.89, precision being 0.92, recall being 0.82, and F1-score being 0.87, which outperformed the correlation-based hypertension detection. Conclusions: The results indicate that the causal inference-based approach can potentially clarify the mechanism of hypertension detection with noninvasive signal and effectively detect hypertension. In addition, the results also re-veal that causality is more reliable and effective compared to correlation.