Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring

Maria G Signorini, Nicolò Pini, Alberto Malovini, Riccardo Bellazzi, Giovanni Magenes

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND AND OBJECTIVES: Intrauterine Growth Restriction (IUGR) is a fetal condition defined as the abnormal rate of fetal growth. The pathology is a documented cause of fetal and neonatal morbidity and mortality. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. Therefore, designing an accurate model for the early and prompt identification of pathology in the antepartum period is crucial in view of pregnancy management.

METHODS: We tested the performance of 15 machine learning techniques in discriminating healthy versus IUGR fetuses. The various models were trained with a set of 12 physiology based heart rate features extracted from a single antepartum CardioTocographic (CTG) recording. The reason for the utilization of time, frequency, and nonlinear indices is based on their standalone documented ability to describe several physiological and pathological fetal conditions.

RESULTS: We validated our approach on a database of 60 healthy and 60 IUGR fetuses. The machine learning methodology achieving the best performance was Random Forests. Specifically, we obtained a mean classification accuracy of 0.911 [0.860, 0.961 (0.95 confidence interval)] averaged over 10 test sets (10 Fold Cross Validation). Similar results were provided by Classification Trees, Logistic Regression, and Support Vector Machines. A features ranking procedure highlighted that nonlinear indices showed the highest capability to discriminate between the considered fetal conditions. Nevertheless, is the combination of features investigating CTG signal in different domains, that contributes to an increase in classification accuracy.

CONCLUSIONS: We provided validation of an accurate artificially intelligence framework for the diagnosis of IUGR condition in the antepartum period. The employed physiology based heart rate features constitute an interpretable link between the machine learning results and the quantitative estimators of fetal wellbeing.

Original languageEnglish
Pages (from-to)105015
JournalComputer Methods and Programs in Biomedicine
Volume185
DOIs
Publication statusPublished - Mar 2020

Keywords

  • Cardiotocography/methods
  • Female
  • Fetal Monitoring/methods
  • Heart Rate, Fetal
  • Humans
  • Machine Learning
  • Pregnancy
  • Reproducibility of Results
  • Systems Integration

Fingerprint

Dive into the research topics of 'Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring'. Together they form a unique fingerprint.

Cite this