A machine learning approach for hypertension detection based on photoplethysmography and clinical data

• Published in: Computers in biology and medicine Vol. 145; p. 105479
• Main Authors: Martinez-Ríos, Erick, Montesinos, Luis, Alfaro-Ponce, Mariel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2022; Elsevier Limited
• Subjects: Age; Blood Pressure; Body mass index; Body size; Classification; Computer applications; Data analysis; Datasets; Deep learning; Discriminant analysis; Electrocardiography; Feature extraction; Feature selection; Heart rate; Humans; Hypertension; Hypertension - diagnosis; Internal Medicine; Learning algorithms; Machine Learning; Morphology; Neural networks; Noise; Other; Photoplethysmography - methods; Physiology; Prehypertension; Risk stratification; Scattering; Signal quality; Sociodemographics; Support vector machines; Wavelet transform; Wavelet transforms; Blood pressure; Wavelet transform; Classification; Machine learning; Risk stratification

High blood pressure early screening remains a challenge due to the lack of symptoms associated with it. Accordingly, noninvasive methods based on photoplethysmography (PPG) or clinical data analysis and the training of machine learning techniques for hypertension detection have been proposed in the...