Evolving Blood Pressure Estimation: From Feature Analysis to Image-Based Deep Learning Models.
Traditional cuffless blood pressure (BP) estimation methods often require collecting physiological signals, such as electrocardiogram (ECG) and photoplethysmography (PPG), from two distinct body sites to compute metrics like pulse transit time (PTT) or pulse arrival time (PAT). While these metrics s...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 17 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
7/9/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186488960&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186488960 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 7/9/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 186488960 186488960 186488960 10.1007/s10916-025-02228-6 186488960 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evolving Blood Pressure Estimation: From Feature Analysis to Image-Based Deep Learning Models. aug: au: Roha, Vishal Singh Ranjan, Rahul Yuce, Mehmet Rasit affil: https://ror.org/02bfwt286 Department of Electrical and Computer Systems, Monash University, Wellington Rd, Clayton, 3800, Melbourne, VIC, Australia sug: subj: Blood Pressure Determination Methods Deep Learning Plethysmography Prediction Models Blood Pressure Signal Processing, Computer Assisted Human Pulse Wave Velocity Electrocardiography Descriptive Statistics Systolic Pressure Diastolic Pressure Benchmarking ab: Traditional cuffless blood pressure (BP) estimation methods often require collecting physiological signals, such as electrocardiogram (ECG) and photoplethysmography (PPG), from two distinct body sites to compute metrics like pulse transit time (PTT) or pulse arrival time (PAT). 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. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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