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Details

  • Name

    Maria Luísa Morais
  • Cluster

    Computer Science
  • Role

    Researcher
  • Since

    07th January 2019
Publications

2019

On the prediction of foetal acidaemia: A spectral analysis-based approach

Authors
Zarmehri, MN; Castro, L; Santos, J; Bernardes, J; Costa, A; Santos, CC;

Publication
COMPUTERS IN BIOLOGY AND MEDICINE

Abstract
A computational analysis of physiological systems has been used to support the understanding of how these systems work, and in the case of foetal heart rate, many different approaches have been developed in the last decades. Our objective was to apply a new method of classification, which is based on spectral analysis, in foetal heart rate (FHR) traces to predict foetal acidosis diagnosed with umbilical arterial blood pH <= 7.05. Fast Fourier transform was applied to a real database for the classification approach. To evaluate the models, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were used. Sensitivity equal to 1, specificity equal to 0.85 and an area under the ROC curve of 0.94 were found. In addition, when the definition of metabolic acidosis of umbilical arterial blood pH <= 7.05 and base excess <= -10 mmol/L was used, the proposed methodology obtained sensitivity = 1, specificity = 0.97 and area under the ROC curve = 0.98. The proposed methodology relies exclusively on the spectral frequency decomposition of the FHR signal. After further successful validation in more datasets, this approach can be incorporated easily in clinical practice due to its simple implementation. Likewise, the incorporation of this novel technique in an intrapartum monitoring station should be straightforward, thus enabling the assistance of labour professionals in the anticipated detection of acidaemia.

2018

Towards FHR Biometric Identification: A Comparison between Compression and Entropy Based Approaches

Authors
Castro, L; Teixeira, A; Brás, S; Santos, M; Costa Santos, C;

Publication
Proceedings - IEEE Symposium on Computer-Based Medical Systems

Abstract
In this study, fetal heart rate signal is used to exemplify the performance of compression and entropy based approaches in biometric identification. A total of 167 pairs of traces from real fetus are analyzed under the popular normalized compression distance, the recently proposed normalized relative compression measure and mutual information measure. The best performance was achieved with the normalized compression distance resulting in a misclassification rate of 12%. Fetal heart rate could be a relevant feature for biometric identification models, namely in multiple pregnancies. © 2018 IEEE.