Separating sets of term and pre-term uterine EMG records

The analysis of uterine EMG (electrohysterogram-EHG) records may help solve the problem of predicting pre-term labor. We investigated the adaptive autoregressive (AAR) method to estimate the EHG signal spectrograms and sample entropy, to separate and classify sets of term and pre-term delivery recor...

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Bibliographic Details
Published in:Physiological measurement 2015-02, Vol.36 (2), p.341-355
Main Authors: Smrdel, A, Jager, F
Format: Article
Language:English
Subjects:
adaptive autoregressive method
Algorithms
Delivery, Obstetric
Electromyography - methods
Entropy
Female
Humans
pre-term labor prediction
Pregnancy
Premature Birth - physiopathology
sample entropy
Signal Processing, Computer-Assisted
Term Birth - physiology
Term-Preterm EHG database
uterine electromyogram
Uterus - physiology
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Summary:The analysis of uterine EMG (electrohysterogram-EHG) records may help solve the problem of predicting pre-term labor. We investigated the adaptive autoregressive (AAR) method to estimate the EHG signal spectrograms and sample entropy, to separate and classify sets of term and pre-term delivery records, using the Term-Preterm EHG Database. The database contains four sets of records divided according to the time of delivery (term or pre-term: 37 or < 37 weeks of gestation, respectively) and according to the time of recording (early or later: before or after the 26th week of gestation, respectively). Using the AAR method the term and pre-term delivery records recorded early can be separated (p = 0.002), as well as all term and pre-term delivery records (p < 0.001). Using the sample entropy, the results showed that all term and pre-term delivery records can be separated (p = 0.022). The spectra of the signals for term delivery records have the tendency of moving to lower frequencies as the time of pregnancy increases. We investigated a few classifiers to classify records between term and pre-term delivery sets. Using median frequency measurements and additional clinical information with the synthetic minority over-sampling technique, the quadratic discriminant analysis classifier achieved a 97% classification accuracy for the records recorded early, and 86% for all records regardless of the time of recording; while for the sample entropy measurements, for the same sets of records, using the support vector machine classifier, the classification accuracies were 80% and 87%, respectively.
ISSN:0967-3334
1361-6579
DOI:10.1088/0967-3334/36/2/341