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

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.