Subject: Feature extraction
Subject: Machine Learning
Subject: Oxygen saturation
Subject: Photoplethysmogram
Subject: Predictive models
Year: 2022
Type: Article
Title: Machine learning based SpO2 prediction from PPG signal's characteristics features
Author: Koteska, Bojana
Author: Mitrova, Hristina
Author: Madevska Bogdanova, Ana
Author: Lehocki, Fedor
Abstract: Continuous monitoring of blood oxygen saturation level (SpO2) during the second triage in the high casualty event and determining the hemostability of a patient/victim until arrival to a medical facility, is essential in emergency situations. Using a SmartPatch device attached to a victim's chest that contains a Photoplethysmogram Waveforms (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the SmartPatch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the Sp02 by extracting the set of features from the PPG signal utilizing two Python toolkits, HeartPy and Neurokit, in order to model the Machine learning predictors, using multiple regressors. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 – 100%. The best experimental results - MAE (1.45), MSE (3.85), RMSE (1.96) and RMSLE (0.02) scores are achieved with the Random Forest regressor in the experiment with 7 features extracted from the both toolkits.
Publisher: IEEE
Relation: "Smart Patch for Life Support Systems" - NATO project G5825 SP4LIFE
Identifier: oai:repository.ukim.mk:20.500.12188/22662
Identifier: http://hdl.handle.net/20.500.12188/22662Identifier: 10.1109/memea54994.2022.9856498
Identifier: http://xplorestaging.ieee.org/ielx7/9855895/9856402/09856498.pdf?arnumber=9856498
