An EHG-based Preterm Delivery Prediction Algorithm via Convolution Neural Network.
10.3969/j.issn.1671-7104.2022.03.002
- Author:
Shen-Guan WU
1
;
Yan-Jun DENG
2
;
Ye-Fei ZHANG
2
;
Li-Huan SHAO
2
;
Zhi-Dong ZHAO
2
Author Information
1. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018.
2. School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, 310018.
- Publication Type:Journal Article
- Keywords:
AlexNet;
Gramian angular difference field(GADF);
deep convolution neural network(DCNN);
electrohysterography(EHG)
- MeSH:
Algorithms;
Electromyography;
Female;
Humans;
Infant, Newborn;
Neural Networks, Computer;
Pregnancy;
Premature Birth/diagnosis*;
Uterine Contraction
- From:
Chinese Journal of Medical Instrumentation
2022;46(3):242-247
- CountryChina
- Language:Chinese
-
Abstract:
Premature delivery is one of the direct factors that affect the early development and safety of infants. Its direct clinical manifestation is the change of uterine contraction intensity and frequency. Uterine Electrohysterography(EHG) signal collected from the abdomen of pregnant women can accurately and effectively reflect the uterine contraction, which has higher clinical application value than invasive monitoring technology such as intrauterine pressure catheter. Therefore, the research of fetal preterm birth recognition algorithm based on EHG is particularly important for perinatal fetal monitoring. We proposed a convolution neural network(CNN) based on EHG fetal preterm birth recognition algorithm, and a deep CNN model was constructed by combining the Gramian angular difference field(GADF) with the transfer learning technology. The structure of the model was optimized using the clinical measured term-preterm EHG database. The classification accuracy of 94.38% and F1 value of 97.11% were achieved. The experimental results showed that the model constructed in this paper has a certain auxiliary diagnostic value for clinical prediction of premature delivery.
