Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals

• Peng Ren ,

  * E-mail: pren28@uestc.edu.cn (PR); k.kendrick.uestc@gmail.com (KMM)

  Affiliation Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China

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• Shuxia Yao,

  Affiliation Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China

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• Jingxuan Li,

  Affiliation School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

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• Pedro A. Valdes-Sosa,

  Affiliation Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China

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• Keith M. Kendrick

  * E-mail: pren28@uestc.edu.cn (PR); k.kendrick.uestc@gmail.com (KMM)

  Affiliation Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China

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