Title: Dual pipeline technique for detecting sepsis from photoplethysmography

Authors: Shadi Abudalfa; Sara Lombardi; Eleonora Barcali; Leonardo Bocchi

Addresses: SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Kingdom of Saudi Arabia ' Department of Information Engineering, University of Florence, Florence, 50139, Italy ' Department of Information Engineering, University of Florence, Florence, 50139, Italy ' Department of Information Engineering, University of Florence, Florence, 50139, Italy

Abstract: The goal of this work is to improve the performance of sepsis-detection in photoplethysmography (PPG) data. To achieve this goal, we present a hybrid technique for classifying sepsis in PPG data based on confident learning (CL) with noisy data. The technique presented in this study employs CL to improve the accuracy and reliability of the machine learning models, as it takes into account the uncertainty associated with each prediction. Numerous experiments were carried out to assess the performance of the presented technique in detecting sepsis using PPG data. The results obtained, using the best-performing XGBoost model, were compared with those of a previous study in which a deep learning-based model was applied to the same sample of data. The presented technique demonstrated its effectiveness by achieving an F1-score of 80.62% on test set, with a 7% improvement compared to the performance of the previous study.

Keywords: confident learning; rich features; noisy data; photoplethysmography; sepsis; synthetic data generation.

DOI: 10.1504/IJDMB.2025.147058

International Journal of Data Mining and Bioinformatics, 2025 Vol.29 No.3, pp.362 - 380

Received: 26 Nov 2023
Accepted: 26 Jun 2024
Published online: 10 Jul 2025 *

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