Machine learning based SpO2 prediction from PPG signal's characteristics features

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 che...

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Published in:2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA) pp. 1 - 6
Main Authors: Koteska, Bojana, Mitrova, Hristina, Bogdanova, Ana Madevska, Lehocki, Fedor
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2022
Subjects:
Decision making
Feature extraction
Filtering
machine learning
Neural networks
oxygen saturation
photoplethysmogram data
Pipelines
Predictive models
Prototypes
signal processing
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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.
AbstractList 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.
Author Koteska, Bojana
Lehocki, Fedor
Bogdanova, Ana Madevska
Mitrova, Hristina
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  fullname: Koteska, Bojana
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  givenname: Fedor
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  fullname: Lehocki, Fedor
  email: fedor.lehocki@stuba.sk
  organization: Institute of Measurement Science, Slovak Academy of Sciences,Bratislava,Slovakia
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SubjectTerms Decision making
Feature extraction
Filtering
machine learning
Neural networks
oxygen saturation
photoplethysmogram data
Pipelines
Predictive models
Prototypes
signal processing
Title Machine learning based SpO2 prediction from PPG signal's characteristics features
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