Feature Analysis of Uterine Electrohystography Signal Using Dynamic Self-organised Multilayer Network Inspired by the Immune Algorithm

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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Communication dans un congrès
10th International Conference, ICIC 2014, Aug 2014, Taiyuan, China. Proceedings of the 10th International Conference, ICIC 2014, 8588, pp.206-212, 2014, Intelligent Computing Theory. 〈10.1007/978-3-319-09333-8_22〉
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https://hal-supelec.archives-ouvertes.fr/hal-01104317
Contributeur : Alexandra Siebert <>
Soumis le : vendredi 16 janvier 2015 - 15:12:17
Dernière modification le : jeudi 29 mars 2018 - 11:06:05

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Haya Alaskar, Abir Jaafar Hussain, Paul Fergus, Dhiya Al-Jumeily, Hissam Tawfik, et al.. Feature Analysis of Uterine Electrohystography Signal Using Dynamic Self-organised Multilayer Network Inspired by the Immune Algorithm. 10th International Conference, ICIC 2014, Aug 2014, Taiyuan, China. Proceedings of the 10th International Conference, ICIC 2014, 8588, pp.206-212, 2014, Intelligent Computing Theory. 〈10.1007/978-3-319-09333-8_22〉. 〈hal-01104317〉

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