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Guidelines For Measuring Vital Signs01:19

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Related Experiment Video

Updated: Aug 23, 2025

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care
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Classifying sepsis from photoplethysmography.

Sara Lombardi1, Petri Partanen2, Piergiorgio Francia1

  • 1Department of Information Engineering, University of Florence, Via S. Marta, 3, 50139 Florence, Italy.

Health Information Science and Systems
|November 4, 2022
PubMed
Summary

Early sepsis detection is possible using deep learning models analyzing photoplethysmographic (PPG) signals from pulse oximeters. This non-invasive method shows promise for continuous patient monitoring and faster therapeutic intervention.

Keywords:
CNNDeep learningICUPPGPhotoplethysmographySepsis

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Critical Care Medicine

Background:

  • Sepsis is a life-threatening organ dysfunction and a leading cause of intensive care unit (ICU) mortality.
  • Early detection and treatment are crucial for improving sepsis patient survival rates.
  • Continuous monitoring tools are needed for timely sepsis evaluation.

Purpose of the Study:

  • To investigate the feasibility of detecting sepsis using photoplethysmographic (PPG) signals from pulse oximeters.
  • To develop and validate a deep learning model for binary classification of septic and non-septic patients.
  • To assess the potential of PPG signals as an early warning sign for sepsis.

Main Methods:

  • A deep learning model was developed for sepsis identification.
  • The model utilized PPG signals acquired via pulse oximeter as input.
  • The MIMIC-III database, containing data from 85 septic and 101 control ICU patients, was used for model development and testing.

Main Results:

  • The deep learning model achieved an accuracy of 76.37% on the test set.
  • Sensitivity was 70.95% and specificity was 81.04%.
  • The Area Under the ROC Curve (AUC) reached 0.842, indicating good discriminatory performance.

Conclusions:

  • Photoplethysmographic signals show potential as a non-invasive, early warning sign for sepsis detection.
  • The developed deep learning method can aid in reducing diagnosis and therapeutic intervention times.
  • The proposed approach is suitable for integration into continuous patient monitoring systems in critical care settings.