Deep neural network for semi-automatic classification of term and preterm uterine recordings

@article{Chen2020DeepNN,
  title={Deep neural network for semi-automatic classification of term and preterm uterine recordings},
  author={L. Chen and Huoyao Xu},
  journal={Artificial intelligence in medicine},
  year={2020},
  volume={105},
  pages={
          101861
        }
}
  • L. Chen, Huoyao Xu
  • Published 2020
  • Computer Science, Medicine
  • Artificial intelligence in medicine
  • Pregnancy is a complex process, and the prediction of premature birth is uncertain. Many researchers are exploring non-invasive approaches to enhance its predictability. Currently, the ElectroHysteroGram (EHG) and Tocography (TOCO) signal are a real-time and non-invasive technology which can be employed to predict preterm birth. For this purpose, sparse autoencoder (SAE) based deep neural network (SAE-based DNN) is developed. The deep neural network has three layers including a stacked sparse… CONTINUE READING

    Topics from this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 47 REFERENCES