Publication detail

Blood pressure estimation using smartphone

ŠÍMA, J. NĚMCOVÁ, A.

Original Title

Blood pressure estimation using smartphone

Type

conference paper

Language

English

Original Abstract

This paper presents an experimental cuff-less measurement of systolic (SBP) and diastolic blood pressure (DBP) using smartphone. A photoplethysmographic signal (PPG) measured by a smartphone camera is used to estimate blood pressure (BP). This paper contains comparison of several machine learning (ML) methods for BP estimation. Filtering the PPG signal with a band-pass filter (0.5-12 Hz) followed by feature extraction and using Random Forest (RF) methods separately or as a weak regressor in adaptive boosting (AdaBoost) or bootstrap aggregating (Boosting) reached the best results according to Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standards among all regression ML models. The mean absolute error (MAE) and standard deviation (SD) of Bagging model were 4.532±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 measurement of blood pressure;machine learning

Authors

ŠÍMA, J.; NĚMCOVÁ, A.

Released

25. 4. 2023

Publisher

Brno University of Technology, Faculty of Electrical Engineering and Communication

Location

Brno

ISBN

978-80-214-6154-3

Book

Proceedings II of the 29 th Conference STUDENT EEICT 2023 Selected papers

Edition

1st Edition

ISBN

2788-1334

Periodical

Proceedings II of the Conference STUDENT EEICT

State

Czech Republic

Pages from

129

Pages to

132

Pages count

4

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf

BibTex

@inproceedings{BUT184325,
  author="Jan {Šíma} and Andrea {Němcová}",
  title="Blood pressure estimation using smartphone",
  booktitle="Proceedings II of the 29 th Conference STUDENT EEICT 2023 Selected papers",
  year="2023",
  series="1st Edition",
  journal="Proceedings II of the Conference STUDENT EEICT",
  pages="129--132",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  doi="10.13164/eeict.2023.129",
  isbn="978-80-214-6154-3",
  issn="2788-1334",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf"
}