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While these metrics strongly correlate with BP, their reliance on multiple signal sources and susceptibility to noise from modern wearable devices present significant challenges. Addressing these limitations, we propose an innovative framework that requires only PPG signals from a single body site, leveraging advancements in artificial intelligence and computer vision. Our approach employs images of PPG signals, along with their first (vPPG) and second (aPPG) derivatives, for enhanced BP estimation. ResNet-50 is utilized to extract features and identify regions within the PPG, vPPG, and aPPG images that correlate strongly with BP. These features are further refined using multi-head cross-attention (MHCA) mechanism, enabling efficient information exchange across the modalities derived from ResNet-50 outputs, thereby improving estimation accuracy. The framework is validated on three distinct datasets, demonstrating superior performance compared to traditional PAT and PTT-based methods. Furthermore, it adheres to stringent medical standards, such as those defined by the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS), ensuring clinical reliability. By reducing the need for multiple signal sources and incorporating cutting-edge AI techniques, this framework represents a significant advancement in non-invasive BP monitoring, offering a more practical and accurate alternative to traditional methodologies.<\/jats:p>","DOI":"10.1007\/s10916-025-02228-6","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:20:00Z","timestamp":1752139200000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Evolving Blood Pressure Estimation: From Feature Analysis to Image-Based Deep Learning Models"],"prefix":"10.1007","volume":"49","author":[{"given":"Vishal Singh","family":"Roha","sequence":"first","affiliation":[]},{"given":"Rahul","family":"Ranjan","sequence":"additional","affiliation":[]},{"given":"Mehmet Rasit","family":"Yuce","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"2228_CR1","unstructured":"World Health Organization: Hypertension. 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For the Cabrini dataset, the study was approved by the Cabrini Human Research Ethics Committee (CHREC170528) (07-19-06-17) and registered with the ANZ Clinical Trial Registry (ACTRN12617000774325p). All participants provided informed consent before inclusion in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"Not applicable. The data presented in this study are anonymized and do not contain identifiable information.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"97"}}