Abstract
Premature birth is a significant worldwide problem. There is little understanding why premature births occur or the factors that contribute to its onset. However, it is generally agreed that early detection will help to mitigate the effects preterm birth has on the child and in some cases stop its onset. Research in mathematical modelling and information technology is beginning to produce some interesting results and is a line of enquiry that is likely to prove useful in the early prediction of premature births. This paper proposes a new approach which is based on a neural network architecture called Dynamic Self-organised Multilayer Network Inspired by the Immune Algorithm to analyse uterine electrohystography signals. The signals are pre-processed and features are extracted using the neural network and evaluated using the Mean Squared Error, Mean absolute error, and Normalized Mean Squared Error to rank their ability to discriminate between term and preterm records.
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Alaskar, H., Hussain, A.J., Paul, F.H., Al-Jumeily, D., Tawfik, H., Hamdan, H. (2014). Feature Analysis of Uterine Electrohystography Signal Using Dynamic Self-organised Multilayer Network Inspired by the Immune Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_22
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DOI: https://doi.org/10.1007/978-3-319-09333-8_22
Publisher Name: Springer, Cham
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