Respiratory Rate Prediction Algorithm based on Pulse Oximeter

Cetinkaya N., Turhan S. N., PINARER Ö.

2021 IEEE International Conference on Big Data, Big Data 2021, Virtual, Online, United States Of America, 15 - 18 December 2021, pp.4652-4658 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/bigdata52589.2021.9671288
  • City: Virtual, Online
  • Country: United States Of America
  • Page Numbers: pp.4652-4658
  • Keywords: Respiratory Rate, Supervised Machine Learning, Photoplethysmogram, Electrocardiyogram, EXTRACTION, RESOURCE
  • Galatasaray University Affiliated: Yes

Abstract

© 2021 IEEE.Respiratory rate (RR) is a physiological parameter typically used to monitor patient status in clinical settings. The goal of the Respiratory Rate Prediction Project is to use supervised machine learning techniques to estimate a person's respiratory rate using real-time, continuous Photoplethysmogram (PPG) and Electrocardiogram (ECG) and oximeter data. In addition, it is also our goal to investigate the feasibility of using such data to improve diagnostic processes in healthcare. It consists of a series of studies of different algorithms for respiratory rate estimation from clinical data and is complemented by the provision of publicly available datasets and resources.