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, Amerika Birleşik Devletleri, 15 - 18 Aralık 2021, ss.4652-4658 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/bigdata52589.2021.9671288
  • Basıldığı Şehir: Virtual, Online
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.4652-4658
  • Anahtar Kelimeler: Respiratory Rate, Supervised Machine Learning, Photoplethysmogram, Electrocardiyogram, EXTRACTION, RESOURCE
  • Galatasaray Üniversitesi Adresli: Evet

Özet

© 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.