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Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data

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Abstract

Objective

To use artificial neural networks (ANNs) on uterine electromyography (EMG) data to classify term/preterm labor/non-labor pregnant patients.

Materials And Methods

A total of 134 term and 51 preterm women (all ultimately delivered spontaneously) were included. Uterine EMG was measured trans-abdominally using surface electrodes. “Bursts” of elevated uterine EMG, corresponding to uterine contractions, were quantified by finding the means and/or standard deviations of the power spectrum (PS) peak frequency, burst duration, number of bursts per unit time, and total burst activity. Measurement-to-delivery (MTD) time was noted for each patient. Term and preterm patient groups were sub-divided, resulting in the following categories: [term-laboring (TL): n = 75; preterm-laboring (PTL): n = 13] and [term-non-laboring (TN): n = 59; preterm-non-laboring (PTN): n = 38], with labor assessed using clinical determinations. ANN was then used on the calculated uterine EMG data to algorithmically and objectively classify patients into labor and non-labor. The percent of correctly categorized patients was found. Comparison between ANN-sorted groups was then performed using Student’s t test (with p < 0.05 significant).

Results

In total, 59/75 (79%) of TL patients, 12/13 (92%) of PTL patients, 51/59 (86%) of TN patients, and 27/38 (71%) of PTN patients were correctly classified.

Conclusion

ANNs, used with uterine EMG data, can effectively classify term/preterm labor/non-labor patients.

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Acknowlegment

We would like to thank NIH (R01-HD037480) for the funding to complete this research.

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Authors and Affiliations

  1. Department of Obstetrics and Gynecology, University of Texas Medical Branch, 301 University, Route 1062, Galveston, TX, 77555, USA

    William L. Maner & Robert E. Garfield

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  1. William L. Maner
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  2. Robert E. Garfield
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Correspondence to Robert E. Garfield.

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Maner, W.L., Garfield, R.E. Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Ann Biomed Eng 35, 465–473 (2007). https://doi.org/10.1007/s10439-006-9248-8

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  • DOI: https://doi.org/10.1007/s10439-006-9248-8

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