Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units
Computerized Cardiotocography (cCTG), which involves the continuous recording of the fetal heart rate (FHR) and uterine contraction (UC) signals, plays a critical role in the evaluation of antepartum fetal well-being. However, traditional cCTG usually performs the processes of CTG feature extraction and classification separately, which has a certain degree of calibration bias and cannot fully use the CTG information. In this paper, we develop a multimodal bidirectional gated recurrent units (MBiGRU) network for end-to-end CTG feature extraction and classification. Specifically, data preprocessing was first conducted on raw CTG data, including missing values and outliers processing, FHR signal normalization, signal segmentation and enhancement. Afterward, the synchronous FHR, UC, and fetal movement (FetMov) signal fragments were converted into the corresponding two-dimensional fragments through the embedding layers, and then the multimodal fusion of signals was performed in the concatenating layers. Furthermore, classification results were obtained by bidirectional gated recurrent unit (BiGRU) with a fully connected layer and sigmoid function. The effectiveness of the proposed MBiGRU model was tested on 16,355 antenatal CTG records collected from collaborating hospitals with consistent case interpretation by three expert obstetricians. The experimental results of ten-fold cross validation showed that the average accuracy, F1-score, and area under the curve (AUC) values of MBiGRU were 86.45%, 86.14%, and 0.9327, respectively. The MBiGRU improved upon the performance of the baseline BiGRU network with no UC or FetMov signal inputs and outperformed other deep learning methods. In conclusion, the proposed MBiGRU network is promising for intelligent antepartum fetal monitoring.
National Natural Science Foundation of China;National Natural Science Foundation of China;
|EI Accession Number|
Biomedical signal processing ; Classification (of information) ; Deep learning ; Extraction ; Feature extraction ; Learning systems ; Neonatal monitoring ; Obstetrics
|ESI Classification Code|
Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Chemical Operations:802.3 ; Information Sources and Analysis:903.1
Cited Times [WOS]:1
|Document Type||Journal Article|
|Department||Department of Biomedical Engineering|
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 Computer Science and Engineering,Lehigh University,Bethlehem,United States
4.Department of Human Centered Design,Cornell University,Ithaca, NY,United States
5.Guangzhou Medical University Second Affiliated Hospital,Guangzhou,China
6.The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,China
7.Tianhe District People's Hospital,First Affiliated Hospital of Jinan University,Guangzhou,China
8.Guangzhou Sunray Medical Apparatus Co. Ltd,Guangzhou,China
|First Author Affilication||Department of Biomedical Engineering|
Fei，Yue,Chen，Fan,He，Lifang,et al. Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units[J]. Biomedical Signal Processing and Control,2022,78.
Fei，Yue.,Chen，Fan.,He，Lifang.,Chen，Jiamin.,Hao，Yuexing.,...&Wei，Hang.(2022).Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units.Biomedical Signal Processing and Control,78.
Fei，Yue,et al."Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units".Biomedical Signal Processing and Control 78(2022).
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