Hypertension screening via awake–sleep differences in photoplethysmogram signals

Hypertension is a major risk factor for cardiovascular diseases. This study proposes a novel hypertension community-based screening framework based on intra-subject awake–sleep differences in photoplethysmography (PPG) indices, using machine learning. We hypothesized that normotensive individuals exhibit greater PPG variation between awake and sleep states than unmanaged hypertensive individuals.

The Aurora-BP dataset (n = 180; 138 normotensive, 42 hypertensive) was used for model development, with 18 subjects reserved for internal testing. External validation was performed using the independent CUHK-BP dataset (n = 26; 11 normotensive, 15 hypertensive). Twenty PPG-based indices were extracted, and subject-level P-values from Mann–Whitney U-tests comparing awake and sleep periods were used as model features. Discretized P-values served as inputs for four machine learning models. The support vector machine (SVM) achieved the highest performance, with 81.1 ± 8.4% accuracy and 82.8 ± 8.1% F1-score on the internal test set using all indices. On the external test set, the SVM using only temporal indices achieved 84.6% accuracy and 86.7% F1 score. Temporal indices, especially those linked to the dicrotic notch, showed strong generalizability across datasets.

The study demonstrates the feasibility of awake–sleep PPG analysis for hypertension screening, highlighting the potential of wearable PPG devices for ambulatory monitoring.