Hypertension Screening via Awake-Sleep Differences in Photoplethysmogram Signals
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Background
Hypertension is a major risk factor for cardiovascular diseases. This study proposes a novel hypertension screening framework based on awake-sleep differences in photoplethysmography (PPG) indices, using machine learning. We hypothesised that normotensive individuals exhibit greater PPG variation between awake and sleep states than unmanaged hypertensive individuals.
Methods
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. Discretised p-values served as inputs for four machine learning models.
Results
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 generalisability 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.
Novelty and Relevance
What Is New?
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This study introduces a novel machine learning–based hypertension screening method using awake-sleep differences in ambulatory photoplethysmogram (PPG) signals, validated on both internal and external datasets with a novel p-value–based feature selection strategy.
What Is Relevant?
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The proposed approach enables non-invasive, cuff-free hypertension detection using wearable PPG data, offering improved generalisability and real-world applicability over traditional models.
Clinical/Pathophysiological Implications?
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The ability to detect altered nocturnal cardiovascular patterns from PPG may support early identification of unmanaged hypertension and promote more accessible long-term monitoring.
