Characterization of abdominally acquired uterine electrical signals in humans, using a non-linear analytic method

William L. Maner, Lynette B. MacKay, George R. Saade, Robert E. Garfield

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

The present work seeks to determine if a particular non-linear analytic method is effective at quantifying uterine electromyography (EMG) data for estimating the onset of labor. Twenty-seven patients were included, and their uterine EMG was recorded non-invasively for 30 min. The patients were grouped into two sets: G1: labor, N=14; G2: antepartum, N=13. G1 patients all delivered spontaneously within 24 h of recording while G2 patients did not. The uterine electrical signals were analyzed offline by first isolating the uterine-specific frequency range and then randomly selecting "bursts" of uterine electrical activity (each associated with a uterine contraction) from every recording. Wavelet transform was subsequently applied to each of the bursts' traces, and then the fractal dimension (FD) of the resulting transformed EMG burst-trace was calculated (Benoit 1.3, Trusoft). Average burst FD was found for each patient. FD means for G1 and G2 were calculated and compared using t test. FD was significantly higher (P<0.05) for G1: 1.27±0.03 versus G2: 1.25±0.02. The wavelet-decomposition-generated fractal dimension can be used to successfully discern between patients who will deliver spontaneously within 24 h and those who will not, and can be useful for the objective classification of antepartum versus labor patients.

Original languageEnglish (US)
Pages (from-to)117-123
Number of pages7
JournalMedical and Biological Engineering and Computing
Volume44
Issue number1-2
DOIs
StatePublished - Mar 1 2006

    Fingerprint

Keywords

  • Delivery
  • Electromyography
  • Fractal
  • Labor
  • Prediction
  • Uterus
  • Wavelet

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

Cite this