The Digi-NewB project for preterm infant sepsis risk and maturity analysis

Värri, Alpo; Kallonen, Antti; Helander, Elina; Ledesma, Andres; Pladys, Patrick

doi:10.23996/fjhw.69152

Cited by 8 publications

(5 citation statements)

References 7 publications

(7 reference statements)

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“…Sensors monitoring vital signs are prone to motion artefacts, which may result in misleading or irrelevant features. There have been different attempts to clean signals for research purposes 25,55 . However, movements, or lack thereof, can also be used for detecting sepsis, and studies implementing sepsis scores based on clinical signs have reported positive correlations 18,33,35,41 .…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, discarding available information may introduce a bias and result in sub‐optimal depiction 22 . Further, other studies collected data periodically via manual data entry, limiting the sampling frequency and simultaneously increasing the risk of data error 18,40,43,46,55 . The comparison of high resolution vital parameter data (kHz to Hz data) vs manually entered electronic health record data (per hour/day) for sepsis prediction is also warranted.…”

Section: Discussionmentioning

confidence: 99%

“…There have been different attempts to clean signals for research purposes. 25,55 However, movements, or lack thereof, can also be used for detecting sepsis, and studies implementing sepsis scores based on clinical signs have reported positive correlations. 18,33,35,41 Similarly, reducing analysis to stationary sequences, 48 which are known to differ between infants with and without sepsis.…”

Section: Additionally Technical Factors Make Comparison Of Differentmentioning

confidence: 99%

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Neonatal sepsis prediction through clinical decision support algorithms: A systematic review

et al. 2021

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Aim: To systematically summarise the current evidence of employing clinical decision support algorithms (CDSAs) using non-invasive parameters for sepsis prediction in neonates. Methods: A comprehensive search in PubMed, CENTRAL and EMBASE was conducted. Screening, data extraction and risk of bias were performed by two authors. The certainty of the evidence was assessed using GRADE. PROSPERO ID: CRD42020205143. Results: After abstract and full-text screening, 36 studies comprising 18,096 infants were included. Most CDSAs evaluated heart rate (HR)-based parameters. Two publications derived from one randomised-controlled trial assessing HR characteristics reported significant reduction in 30-day septicaemia-related mortality. Thirty-four non-randomised studies found promising yet inconclusive results. Conclusion: Heart rate-based parameters are reliable components of CDSAs for sepsis prediction, particularly in combination with additional vital signs and demographics. However, inconclusive evidence and limited standardisation restricts clinical implementation of CDSAs outside of a controlled research environment. Further experimentation and comparison of parameter combinations and testing of new CDSAs are warranted.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Additionally Technical Factors Make Comparison Of Differentmentioning

confidence: 99%

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Neonatal sepsis prediction through clinical decision support algorithms: A systematic review

et al. 2021

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“…Many different approaches to fHR processing and analysis have been studied. They range from simple feature extraction methods to more sophisticated classification programs and joining research centers from different countries for joint projects, as the Digi-Newb project ( 244 ). Usage of continuous non-invasive evaluation, such as the usage of wearables, have been discussed ( 27 , 245 ) and will contribute to the patient's care improvement since it will improve data gathering, reducing costs of fetal monitoring.…”

Section: Discussionmentioning

confidence: 99%

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

et al. 2021

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The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

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“…These features are used in a commercialized sepsis prediction algorithm named HeRO (Heart Rate Observation) [14]. Motion and respiration related time domain features in [15] and Fourier domain features in [16], [17] have also been shown useful for NSD. A feature extraction method which does not depend on a time window length parameter has been proposed in [18] and uses hidden Markov models (HMM) to detect clinical events relevant for sepsis prediction.…”

Section: Introductionmentioning

confidence: 99%

Classification and feature extraction for neonatal sepsis detection

Honoré¹,

Forsberg²,

Jost³

et al. 2022

Preprint

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In this original study, we investigate the performances of machine learning algorithms on a neonatal sepsis detection task. We consider this work to be of great interest to both engineers and clinicians, as it uses non-invasive, already existing, vital signs monitoring signals in a population of very low birth weight infants at high risk of sepsis. Vital sign variability may indeed represent a general indicator of health and wellbeing and be helpful in the early detection of systematic inflammation such as sepsis. We used state of the art feature extraction technics and evaluate a large variety of binary classification models among which a neural network based generative model. The models were chosen from two main families: discriminative and generative. This enables a comprehensive study of different kinds of traditional and advanced binary classification algorithms. Our study reveals that advanced machine learning models are more robust to changes in the feature extraction pipeline, although linear classifiers have a comparable performance when the feature extraction is tuned. The advanced model performing the best is a neural network based generative model which is a hybrid generative and discriminative model. A large window length when computing the features is beneficial to almost all algorithms, indicating the relevance of frequency domain related features for the neonatal sepsis detection task. Overall we obtain a classification AUROC above 0.85, which makes our prediction models potentially relevant in clinical practice. This will enable earlier therapeutic interventions and thereby reduce morbidity and mortality in infants.

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The Digi-NewB project for preterm infant sepsis risk and maturity analysis

Cited by 8 publications

References 7 publications

Neonatal sepsis prediction through clinical decision support algorithms: A systematic review

Neonatal sepsis prediction through clinical decision support algorithms: A systematic review

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

Classification and feature extraction for neonatal sepsis detection

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