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[An EHG-based Preterm Delivery Prediction Algorithm via Convolution Neural Network].

Shen-Guan Wu¹, Yan-Jun Deng², Ye-Fei Zhang²

¹School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018.

Zhongguo Yi Liao Qi Xie Za Zhi = Chinese Journal of Medical Instrumentation

|June 9, 2022

Summary

This summary is machine-generated.

A new algorithm using Electrohysterography (EHG) signals and deep learning accurately predicts premature delivery. This non-invasive method aids in early detection, improving infant safety and development outcomes.

Keywords:

AlexNet Gramian angular difference field(GADF)deep convolution neural network(DCNN)electrohysterography(EHG)

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Area of Science:

Biomedical Engineering
Maternal-Fetal Medicine
Artificial Intelligence in Healthcare

Background:

Premature delivery poses significant risks to infant development and safety.
Uterine Electrohysterography (EHG) offers a non-invasive method to monitor uterine contractions, surpassing traditional intrauterine pressure catheters.
Effective algorithms for preterm birth recognition using EHG are crucial for perinatal monitoring.

Purpose of the Study:

To develop and validate a novel algorithm for recognizing fetal preterm birth using EHG signals.
To enhance the accuracy and clinical utility of non-invasive preterm birth prediction.

Main Methods:

A deep Convolutional Neural Network (CNN) model was designed.
Gramian Angular Difference Fields (GADF) were integrated with transfer learning techniques.
The CNN model was optimized using a clinical database of term and preterm EHG signals.

Main Results:

The developed algorithm achieved a classification accuracy of 94.38%.
An F1 score of 97.11% was obtained, indicating high precision and recall.
The model demonstrated significant auxiliary diagnostic value in clinical prediction.

Conclusions:

The proposed CNN model effectively utilizes EHG signals for preterm birth recognition.
This non-invasive approach shows promise for improving the clinical prediction of premature delivery.
Further research can integrate this technology for enhanced perinatal care and infant outcomes.