DANNMCTG: Domain-Adversarial Training of Neural Network for multicenter antenatal cardiotocography signal classification

Chen，Li1; Fei，Yue1,2; Quan，Bin1,8; Hao，Yuexing3; Chen，Qinqun1; Liu，Guiqing4; Luo，Xiaomu1; Li，Li5,6; Wei，Hang1,7

2024-08-01

10.1016/j.bspc.2024.106259

Biomedical Signal Processing and Control   影响因子和分区

1746-8094

1746-8108

Intelligent classification of cardiotocography (CTG) based on machine learning (ML), a useful tool to improve the accuracy of fetal abnormality detection, can assist obstetricians with clinical decisions. With the advancement of information technologies and medical devices, there are development opportunities for multicenter clinical research and obtaining more digital CTG signals. However, most of the existing clinical multicenter CTG datasets are partially annotated and have discrepancies which do not satisfy the ML condition of independent identical distribution. Therefore, this paper focuses on an unsupervised domain adaptation (UDA) algorithm to realize cross-domain intelligent classification of multimodal CTG signals. We propose a method dubbed domain adversarial training of neural network for multicenter CTG (DANNMCTG), which mainly consists of a label classifier, a feature extractor and a domain discriminator. To match different distribution of fetal heart rate (FHR), uterine contraction (UC) and fetal movement (FetMov) signals, we condition the domain alignment on label predictions by defining the multi-linear map. For analysis, two datasets from the hospital central station and home monitoring devices were considered as the source and target domains. The results showed that the accuracy value, F1 value and area under the curve (AUC) value of the DANNMCTG were 71.25%, 76.08% and 0.7705, respectively. This method significantly improved the performance of the deep learning models without exploiting any information in the target domain, and outperformed the state-of-the-art UDA algorithms for CTG classification. In summary, the DANNMCTG can effectively mitigate the influence of domain shift for multicenter intelligent prenatal fetal monitoring.

Cardiotocography Domain-adversarial training of neural network Intelligent prenatal fetal monitoring Multicenter study Unsupervised domain adaptation

SCI ; EI

英语

其他

20241415837208

Biomedical signal processing ; Classification (of information) ; Deep learning ; Digital devices ; Fetal monitoring ; Learning systems ; Neural networks

Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Information Sources and Analysis:903.1

2-s2.0-85189023178

Scopus

1.School of Medical Information Engineering,Guangzhou University of Chinese Medicine,Guangzhou,China
2.Department of Biomedical Engineering,Southern University of Science and Technology,Shenzhen,China
3.Department of Human Centered Design,Cornell University,Ithaca,United States
4.First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,China
5.Guangzhou Sunray Medical Apparatus Co. Ltd,Guangzhou,China
6.Tianhe District People's Hospital,First Affiliated Hospital of Jinan University,Guangzhou,China
7.Intelligent Chinese Medicine Research Institute,Guangzhou University of Chinese Medicine,Guangzhou,China
8.National Engineering Laboratory for Big Data System Computing Technology,Shenzhen University,Shenzhen,China

Chen，Li,Fei，Yue,Quan，Bin,et al. DANNMCTG: Domain-Adversarial Training of Neural Network for multicenter antenatal cardiotocography signal classification[J]. Biomedical Signal Processing and Control,2024,94.

Chen，Li.,Fei，Yue.,Quan，Bin.,Hao，Yuexing.,Chen，Qinqun.,...&Wei，Hang.(2024).DANNMCTG: Domain-Adversarial Training of Neural Network for multicenter antenatal cardiotocography signal classification.Biomedical Signal Processing and Control,94.

Chen，Li,et al."DANNMCTG: Domain-Adversarial Training of Neural Network for multicenter antenatal cardiotocography signal classification".Biomedical Signal Processing and Control 94(2024).