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Blood Pressure Estimation From Photoplethysmography by Considering Intra- and Inter-Subject Variabilities: Guidelines for a Fair Assessment.

, , , , , , and . IEEE Access, (2023)

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Automatic segmentation of stroke lesions in T1-weighted magnetic resonance images with convolutional neural networks., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 12465 of SPIE Proceedings, SPIE, (2023)A Machine Learning Approach to Predict Arterial Blood Pressure from Photoplethysmography Signal., , , , , and . CinC, page 1-4. IEEE, (2022)Study of CNN Capacity Applied to Left Ventricle Segmentation in Cardiac MRI., , , and . SN Comput. Sci., 2 (6): 480 (2021)A study of CNN capacity applied to Left Venticle Segmentation in Cardiac MRI., , , and . CoRR, (2021)Blood Pressure Estimation From Photoplethysmography by Considering Intra- and Inter-Subject Variabilities: Guidelines for a Fair Assessment., , , , , , and . IEEE Access, (2023)Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features., , , , , , , and . CoRR, (2023)Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices., , , , , , , and . CoRR, (2023)A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals., , , , , , , and . CoRR, (2023)Exploring the limitations of blood pressure estimation using the photoplethysmography signal., , , , , , and . CoRR, (2024)