Preterm delivery is currently a global concern of maternal and child health, which directly affects infants’ early morbidity, and even death in several severe cases. Therefore, it is particularly important to effectively monitor the uterine contraction of perinatal pregnant women, and to make effective prediction and timely treatment for the possibility of preterm delivery. Electromyography (EHG) signal, an important measurement to predict preterm delivery in clinical practice, shows obvious consistency and correlation with the frequency and intensity of uterine contraction. This paper proposed a deep convolution neural network (DCNN) model based on transfer learning. Specifically, it is based on the VGGNet model, combined with recurrence plot (RP) analysis and transfer learning techniques such as “Fine-tune”, marked as VGGNet19-I3. Optimized with the clinical measured term-preterm EHG database, it showed good auxiliary prediction performances in 78 training and test samples, and achieved a high accuracy of 97.00% in 100 validation samples.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com