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  • A Research of Heart Rate Prediction Model Based on Evolutionary Neural Network

    Author(s)
    Xiao, Feng
    Yuchi, Ming
    Ding, Mingyue
    Jo, Jun Hyung
    Griffith University Author(s)
    Jo, Jun
    Year published
    2011
    Metadata
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    Abstract
    Heart rate (HR) signal analysis is widely used in the medicine and medical research area. Physical activities (PA) are commonly recognized to greatly affect the changes of heart rate. A method of Evolutionary Neural Network -- Neuroevolution of Augmenting Topologies (NEAT) is used to build a PA-based HR predictor model. Through special coding, crossover and mutation operator, NEAT can implement network topology and connectivity weights evolution simultaneously. The common problem in evolutionary neural network, like competing conventions, how to protect the new innovation are effectively solved. The experimental ...
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    Heart rate (HR) signal analysis is widely used in the medicine and medical research area. Physical activities (PA) are commonly recognized to greatly affect the changes of heart rate. A method of Evolutionary Neural Network -- Neuroevolution of Augmenting Topologies (NEAT) is used to build a PA-based HR predictor model. Through special coding, crossover and mutation operator, NEAT can implement network topology and connectivity weights evolution simultaneously. The common problem in evolutionary neural network, like competing conventions, how to protect the new innovation are effectively solved. The experimental results demonstrated the application potential of the approach.
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    Conference Title
    2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI)
    DOI
    https://doi.org/10.1109/ICBMI.2011.40
    Subject
    Neurosciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/45274
    Collection
    • Conference outputs

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