Integrating transfer learning with scalogram analysis for blood pressure estimation from PPG signals

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:33:38Z
Fecha de publicación2025-01-01
ResumenThe blood pressure (BP) estimation plays a crucial role in assessing cardiovascular health and preventing related complications. One of the early warning indicators for heart disorders is elevated blood pressure. Thus, monitoring of blood pressure continuously is needed. The study aims to develop and validate a reliable deep learning-based approach for blood pressure estimation using photoplethysmography from the publicly available database MIMIC-II. The continuous wavelet transform (CWT) was used to transform the photoplethysmogram (PPG) signals into scalograms, which were then input into six different deep learning models: VGG16, ResNet50, InceptionV3, NASNetLarge, InceptionResNetV2 and ConvNeXtTiny. The obtained deep features from each one of these models were employed to estimate BP values using random forest. The proposed approach uses a unique transfer learning framework that integrates deep feature extraction from scalograms with random forest regression, providing a new pathway for blood pressure estimation. The models were assessed using mean absolute error (MAE) and standard deviation (SD) in estimating the systolic and diastolic blood pressure values. Out of six models, ConvNeXtTiny and VGG16 showed good performance. ConvNeXtTiny achieved mean absolute error of 2.95 mmHg and standard deviation of 4.11 mmHg for systolic blood pressure and mean absolute error of 1.66 mmHg and standard deviation of 2.60 mmHg for diastolic blood pressure. The achieved result complies with the clinical standards set by Advancement of Medical Instrumentation Standard (AAMI) and the British Hypertension Society standard (BHS). This can enhance cardiovascular health monitoring with continuous, non-invasive and reliable blood pressure measurement, assisting in early detection of the disease. The suggested method shows that reliable blood pressure estimation from photoplethysmography signals is possible with the use of deep learning and transfer learning. Above all, ConvNeXtTiny offers a dependable method for continuous blood pressure monitoring that satisfies clinical requirements and may help in the early identification of cardiovascular problems.es_ES
Doihttps://doi.org/10.1038/s41598-025-23350-yes_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/5427
Idiomaenes_ES
EditorialNATURE PORTFOLIOes_ES
RelaciónScientific Reportses_ES
URL relacionadohttps://doi.org/10.1038/s41598-025-23350-yes_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteScientific Reports
Palabra claveCardiovascular healthes_ES
Palabra claveRandom forestes_ES
Palabra claveDeep learninges_ES
Palabra clavePhotoplethysmographyes_ES
Palabra claveContinuous wavelet transformes_ES
Palabra claveAnd blood pressure estimatees_ES
TítuloIntegrating transfer learning with scalogram analysis for blood pressure estimation from PPG signalses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorSubramanian, Shyamala
AutorMishra, Sashikala
AutorPatil, Shruti
AutorKolekar, Maheshkumar H.
AutorOrtiz-Rodriguez, Fernando
AutorSubramanian, Shyamalaes_ES
AutorMishra, Sashikalaes_ES
AutorPatil, Shruties_ES
AutorKolekar, Maheshkumar H.es_ES
AutorOrtiz-Rodriguez, Fernandoes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número1es_ES
URL relacionadahttps://doi.org/10.1038/s41598-025-23350-y
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen15es_ES

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