Detecting sepsis from photoplethysmography : strategies for dataset preparation

Lombardi, Sara; Partanen, Petri; Bocchi, Leonardo (2022-09-08)
 
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https://doi.org/10.1109/embc48229.2022.9871973

Lombardi, Sara
Partanen, Petri
Bocchi, Leonardo
Institute of Electrical and Electronics Engineers
08.09.2022

S. Lombardi, P. Partanen and L. Bocchi, "Detecting sepsis from photoplethysmography: strategies for dataset preparation," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 2286-2289, doi: 10.1109/EMBC48229.2022.9871973

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doi:https://doi.org/10.1109/embc48229.2022.9871973
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Abstract

Sepsis is one of the most frequent causes of death in Intensive Care Units, and its prognosis greatly depend on timeliness of diagnosis. MIMIC-III database is a frequent source of data for developing method for automatic sepsis detection. However, the heterogeneity of data jeopardize the feasibility of the task. In this work we propose a selection strategy for generating high quality data suitable for training a sepsis detection system based on the utilization of only plethysmographic data. Clinical relevance A system for detecting sepsis based only on PPG may be potentially at virtually no cost in any case clinicians suspect the possibility of developing sepsis.

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