{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T18:06:53Z","timestamp":1784830013228,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Continuous and noninvasive blood pressure (BP) monitoring is important for hypertension screening and long-term cardiovascular management. Continuous BP estimation using photoplethysmography (PPG) and electrocardiography (ECG) has become a promising alternative to conventional cuff-based measurements. However, handcrafted feature-based methods are sensitive to fiducial point detection, while end-to-end deep models often require large labeled datasets and may ignore subject-specific BP baselines. This study proposes a personalized calibration and hybrid feature fusion framework for continuous BP estimation from ECG and PPG signals. The framework integrates physiologically interpretable handcrafted features, self-supervised waveform representations, and subject-specific prior BP information to predict systolic and diastolic BP. The handcrafted branch extracts pulse transit time, heart rate variability, and PPG morphological descriptors, whereas a masked reconstruction-based self-supervised encoder learns latent waveform embeddings without BP labels. Personalized calibration incorporates base BP from the earliest calibration file through an alpha-weighted strategy, and the fused representation is fed into XGBoost regressors. The framework was evaluated on a private dataset and the BP-UCI dataset, achieving SBP\/DBP MAEs of 7.25\/3.98 mmHg and 7.85\/4.02 mmHg, respectively. Comparative and ablation results indicate improved baseline performance in the current experimental setting, suggesting the methodological feasibility of the proposed framework for small-sample continuous BP estimation. Further validation with larger and more diverse cohorts is still required.<\/jats:p>","DOI":"10.3390\/s26154676","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:13:17Z","timestamp":1784826797000},"page":"4676","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized Calibration and Hybrid Feature Fusion for Continuous Blood Pressure Estimation Using PPG and ECG Signals"],"prefix":"10.3390","volume":"26","author":[{"given":"Zichuan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7438-054X","authenticated-orcid":false,"given":"Hong","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). 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