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

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 group... Full description

Journal Title: Medical and Biological Engineering and Computing 2006, Vol.44(1), pp.117-123
Main Author: Maner, William
Other Authors: MacKay, Lynette , Saade, George , Garfield, Robert
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0140-0118 ; E-ISSN: 1741-0444 ; DOI: 10.1007/s11517-005-0011-3
Link: http://dx.doi.org/10.1007/s11517-005-0011-3
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recordid: springer_jour10.1007/s11517-005-0011-3
title: Characterization of abdominally acquired uterine electrical signals in humans, using a non-linear analytic method
format: Article
creator:
  • Maner, William
  • MacKay, Lynette
  • Saade, George
  • Garfield, Robert
subjects:
  • Uterus
  • Prediction
  • Delivery
  • Labor
  • Electromyography
  • Fractal
  • Wavelet
ispartof: Medical and Biological Engineering and Computing, 2006, Vol.44(1), pp.117-123
description: 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
language: eng
source:
identifier: ISSN: 0140-0118 ; E-ISSN: 1741-0444 ; DOI: 10.1007/s11517-005-0011-3
fulltext: fulltext
issn:
  • 1741-0444
  • 17410444
  • 0140-0118
  • 01400118
url: Link


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titleCharacterization of abdominally acquired uterine electrical signals in humans, using a non-linear analytic method
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subjectUterus ; Prediction ; Delivery ; Labor ; Electromyography ; Fractal ; Wavelet
descriptionThe 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.
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descriptionThe 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.
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abstractThe 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.
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doi10.1007/s11517-005-0011-3
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