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Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...

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Preterm EEG: A Multimodal Neurophysiological Protocol
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Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes:

Jessica Rahman1, Aida Brankovic2, Mark Tracy3

  • 1Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australian e-Health Research Centre, Australia, Sydney, Australia.

Interactive Journal of Medical Research
|August 20, 2024
PubMed
Summary
This summary is machine-generated.

Preprocessing neonatal physiological signals for machine learning models lacks transparency and standardization. Enhancing reporting and methods will improve reproducibility and clinical adoption in neonatal intensive care units.

Keywords:
adverse outcomesmorbidityneonatal intensive care unitphysiological signalspredictive and diagnostic modelspretermsignal analysissignal processing

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Area of Science:

  • Biomedical Engineering
  • Clinical Informatics
  • Neonatology

Background:

  • Computational signal preprocessing is crucial for data-driven clinical decision support systems.
  • Ensuring transparency and reproducibility in preprocessing is vital for clinical adoption and regulatory compliance.
  • Standardized preprocessing is essential for developing reliable software as a medical device for early clinical deterioration detection.

Purpose of the Study:

  • To conduct a scoping review of computational methods for preprocessing neonatal clinical physiological signals.
  • To summarize the state-of-the-art techniques used in the neonatal intensive care unit (NICU) setting.
  • To identify methods used for developing machine learning models to predict adverse outcomes in neonates.

Main Methods:

  • Searched five major databases (PubMed, Web of Science, Scopus, IEEE, ACM) for relevant literature from 2013 to 2023.
  • Identified 3585 papers, screened 2994 by title/abstract, and selected 81 for full-text review.
  • Included 52 eligible studies in the detailed analysis based on predefined criteria.

Main Results:

  • Most studies (54%) focused on prognostic models, utilizing signals like electrocardiograms.
  • Significant heterogeneity was observed in data acquisition systems and programming languages (MATLAB, Python).
  • A lack of transparency in reporting signal preprocessing methods (e.g., handling missing data, segment size) was prevalent, with only 13% reporting all recommended steps.

Conclusions:

  • Heterogeneity and inconsistent reporting of preprocessing techniques for neonatal physiological signals hinder adherence to clinical and quality management standards.
  • Improving transparency and standardizing procedures are essential for enhancing study interpretation, reproducibility, and clinical translation.
  • Standardization will build confidence in research findings and accelerate the adoption of advanced neonatal care technologies.