Research on intelligent fetal heart monitoring model based on deep active learning.
10.7507/1001-5515.202402012
- Author:
Bin QUAN
1
;
Yajing HUANG
1
;
Yanfang LI
2
;
Qinqun CHEN
1
;
Honglai ZHANG
1
;
Li LI
3
;
Guiqing LIU
2
;
Hang WEI
1
Author Information
1. School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China.
2. First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510405, P. R. China.
3. Guangzhou Sunray Medical Apparatus Co. Ltd, Guangzhou 510620, P. R. China.
- Publication Type:Journal Article
- Keywords:
Convolution neural network;
Deep active learning;
Intelligent fetal heart monitoring;
Multi-objective optimization;
Three-way decision
- MeSH:
Humans;
Pregnancy;
Female;
Cardiotocography/methods*;
Deep Learning;
Neural Networks, Computer;
Algorithms;
Fetal Monitoring/methods*;
Heart Rate, Fetal;
Fetal Distress/diagnosis*;
Fetal Heart/physiology*
- From:
Journal of Biomedical Engineering
2025;42(1):57-64
- CountryChina
- Language:Chinese
-
Abstract:
Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
