Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals.
OBJECTIVE
This study was undertaken to use artificial neural networks on uterine electromyography data to identify term and preterm labor in rats.


METHODS
Controls (group 1: n = 4) and preterm labor models (group 2: n = 4, treated with onapristone) were used. Uterine electromyography and intrauterine pressure (IUP) variables were measured by implanted telemetric devices. For each timepoint assessed, either a "labor event" or "nonlabor event" was first assigned by using visual and other means. 112 total labor and nonlabor events were observed. Artificial neural networks were then used with electromyography and intrauterine pressure parameters to attempt algorithmic, objective identification for time of labor in each group.


RESULTS
For group 1, all 8 (100%) labor events and all 44 (100%) nonlabor events were correctly identified by the artificial neural networks. For group 2, 22 of 24 (92%) labor events and 31 of 36 (86%) nonlabor events were correctly determined by the artificial neural networks.


CONCLUSIONS
Artificial neural networks can effectively predict term and preterm labor during pregnancy with the use of uterine electromyography and intrauterine pressure variables.
Shi
Shao-Qing
SQ
Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX 77555, USA.
Maner
William L
WL
Mackay
Lynette B
LB
Garfield
Robert E
RE
D000818:Animals; D004576:Electromyography; D005260:Female; D023421:Models, Animal; D016571:Neural Networks, Computer; D007752:Obstetric Labor, Premature / Q000175:diagnosis / Q000503:physiopathology*; D011247:Pregnancy; D011296:Prenatal Diagnosis; D011312:Pressure; D051381:Rats; D014590:Uterine Contraction / Q000502:physiology*; D014599:Uterus / Q000502:physiology*