European heart journal. Digital health · 2026
Hypertension screening via awake-sleep differences in photoplethysmogram signals.
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Hong Jingyuan, Nandi Manasi, Zheng Yali, Alastruey Jordi
Аннотация
AIMS: 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.
METHODS AND RESULTS: 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.
CONCLUSION: The study demonstrates the feasibility of awake-sleep PPG analysis for hypertension screening, highlighting the potential of wearable PPG devices for ambulatory monitoring.
METHODS AND RESULTS: 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.
CONCLUSION: The study demonstrates the feasibility of awake-sleep PPG analysis for hypertension screening, highlighting the potential of wearable PPG devices for ambulatory monitoring.