MDFSBP: A multi-perspective differential feature space framework for estimating blood pressure using photoplethysmography (PPG)

Continuous and accurate blood pressure (BP) monitoring is critical for personalized hypertension management. However, most existing methods focus on absolute BP estimation, with limited attention to BP changes. To address this limitation, we propose a novel framework named Multi-Perspective Differential Feature Space (MDFSBP) for cuffless BP estimation using photoplethysmography (PPG) signals. MDFSBP extracts three perspective differential features: time-based and points-of-interest features, frequency-domain features, and physiological statistical features. Then, an adaptive Multi-Perspective Differential Feature Mapping Module (MDFMM) integrates reconstruction regularization, trend-aware classification, and self-weighted contrastive learning to enhance feature representation and strengthen the association between features and BP changes. Finally, an AutoML-based regression pipeline automates model optimization, improving predictive accuracy and deployment efficiency. To better test the model’s capability in capturing BP changes, we introduce a novel abnormality-aware classification metric. We demonstrate BP estimation performance over state-of-the-art (SOTA) methods on both the Mindray and MIMIC datasets. On the Mindray dataset, the model achieves a regression error of 0.17 ± 5.17 mmHg for SBP and 0.05 ± 3.29 mmHg for DBP, with classification accuracy and F1-score reaching 85.25% and 87.50%, respectively. On the MIMIC dataset, it achieves −0.09 ± 5.70 mmHg for SBP and 0.12 ± 4.27 mmHg for DBP, with the classification accuracy and F1-score of 72.84% and 70.66%, respectively. These results highlight the effectiveness, robustness, and generalizability of the proposed framework for non-invasive, real-time, and continuous BP monitoring in both clinical and wearable healthcare systems. • Propose MDFSBP for cuffless BP estimation using PPG. • Build differential features in time, frequency and statistics. • MDFMM integrates reconstruction, contrastive, and trend tasks. • AutoML regression enables efficient model selection and deployment. • Method outperforms existing approaches on two datasets.