Original title: Blood pressure estimation using smartphone
Authors: Šíma, Jan ; Němcová, Andrea
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: This paper presents an experimental cuff-less measurementof systolic (SBP) and diastolic blood pressure (DBP)using smartphone. A photoplethysmographic signal (PPG) measuredby a smartphone camera is used to estimate blood pressure(BP). This paper contains comparison of several machinelearning (ML) methods for BP estimation. Filtering the PPGsignal with a band-pass filter (0.5-12 Hz) followed by featureextraction and using Random Forest (RF) methods separatelyor as a weak regressor in adaptive boosting (AdaBoost) or bootstrapaggregating (Boosting) reached the best results accordingto Association for the Advancement of Medical Instrumentation(AAMI) and British Hypertension Society (BHS) standardsamong all regression ML models. The mean absolute error(MAE) and standard deviation (SD) of Bagging model were4.532±3.760 mmHg for SBP and 2.738±3.032 mmHg for DBP(AAMI). This result meets the criteria of the AAMI standard.
Keywords: blood pressure estimation; cuff-less measurementof blood pressure; machine learning
Host item entry: Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers, ISBN 978-80-214-6154-3, ISSN 2788-1334

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/210673

Permalink: http://www.nusl.cz/ntk/nusl-531779


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