University of Oulu

Lombardi, S., Partanen, P., Francia, P. et al. Classifying sepsis from photoplethysmography. Health Inf Sci Syst 10, 30 (2022).

Classifying sepsis from photoplethysmography

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Author: Lombardi, Sara1; Partanen, Petri2; Francia, Piergiorgio1;
Organizations: 1Department of Information Engineering, University of Florence, Via S. Marta, 3, 50139, Florence, Italy
2Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, 90570, Oulu, Finland
3S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053, Empoli, Italy
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
Persistent link:
Language: English
Published: Springer Nature, 2022
Publish Date: 2023-02-24


Purpose: Sepsis is a life-threatening organ dysfunction. It is caused by a dysregulated immune response to an infection and is one of the leading causes of death in the intensive care unit (ICU). Early detection and treatment of sepsis can increase the survival rate of patients. The use of devices such as the photoplethysmograph could allow the early evaluation in addition to continuous monitoring of septic patients. The aim of this study was to verify the possibility of detecting sepsis in patients from whom the photoplethysmographic signal was acquired via a pulse oximeter. In this work, we developed a deep learning-based model for sepsis identification. The model takes a single input, the photoplethysmographic signal acquired by pulse oximeter, and performs a binary classification between septic and nonseptic samples. To develop the method, we used MIMIC-III database, which contains data from ICU patients. Specifically, the selected dataset includes 85 septic subjects and 101 control subjects. The PPG signals acquired from these patients were segmented, processed and used as input for the developed model with the aim of identifying sepsis. The proposed method achieved an accuracy of 76.37% with a sensitivity of 70.95% and a specificity of 81.04% on the test set. As regards the ROC curve, the Area Under Curve reached a value of 0.842. The results of this study indicate how the plethysmographic signal can be used as a warning sign for the early detection of sepsis with the aim of reducing the time for diagnosis and therapeutic intervention. Furthermore, the proposed method is suitable for integration in continuous patient monitoring.

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Series: Health information science and systems
ISSN: 2047-2501
ISSN-E: 2047-2501
ISSN-L: 2047-2501
Volume: 10
Issue: 1
Article number: 30
DOI: 10.1007/s13755-022-00199-3
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Funding: Open access funding provided by Università degli Studi di Firenze within the CRUI-CARE Agreement.
Copyright information: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit