Detecting sepsis from photoplethysmography: strategies for dataset preparation

Lombardi, Sara; Partanen, Petri; Bocchi, Leonardo

doi:10.1109/embc48229.2022.9871973

Cited by 4 publications

(2 citation statements)

References 9 publications

(9 reference statements)

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“…In fact, patients with severe clinical conditions could show more complex microcirculatory alterations with different characteristics of the PPG signal. In this sense, in our recent studies we have investigated the relationship between PPG signal and Sepsi conditions in addition to Covid-19 disease [39] , [40] . The fact that many factors can contribute to hindering the analysis of individual PPGs obtained from patients with Covid-19 is to be considered.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 detection using a model of photoplethysmography (PPG) signals

Rossi¹,

Aliani²,

Francia³

et al. 2022

Medical Engineering & Physics

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Section: Discussionmentioning

confidence: 99%

COVID-19 detection using a model of photoplethysmography (PPG) signals

Rossi¹,

Aliani²,

Francia³

et al. 2022

Medical Engineering & Physics

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“…The MIMIC-III database contains a large heterogeneity of subjects, allowing it to be used for a variety of analytical studies. However, this heterogeneity could make the development of an efficient machine learning algorithm challenging [40]. Moreover, diagnosis are reported only as an ICD-code generated at the end of the hospitalization, without providing any information on the date of the diagnosis.…”

Section: Datasetmentioning

confidence: 99%

Classifying sepsis from photoplethysmography

Lombardi

Partanen

Francia

et al. 2022

Health Inf Sci Syst

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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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