Abstract
Seismocardiogram (SCG) recording is a novel method for the prolonged monitoring of the cardiac mechanical performance during spontaneous behavior. Thousands of beats are recorded during a variety of physical activities so that the automatic analysis and processing of such data is a challenging task due to the presence of artefactual beats and morphological changes over time that currently request the human expertise. We propose the use of the evolving fuzzy neural network (EFuNN) paradigm for the automatic artifact prediction in the SCG signal. The fuzzy logic processing method can be applied to model the human expertise knowledge using the learning capabilities of an artificial neural network. The evolving capability of the EFuNN paradigm has been applied to solve the issue of the physiological variability of the SGC waveform. Tests have been carried out to validate this approach. The obtained results demonstrate that the EFuNN’s evolving capabilities are effective to solve most of the issues related to the learning and to the scalability of the method on an off-the shelf computing platform.
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Acknowledgements
A special acknowledgment is due to Prof. Nikola Kasabov, Auckland University of Technology, Director KEDRI—Knowledge Engineering and Discovery Research Institute for his invaluable suggestions related to how to take the best from the EFuNN’s evolving capabilities.
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Malcangi, M., Quan, H., Vaini, E. et al. Evolving fuzzy-neural paradigm applied to the recognition and removal of artefactual beats in continuous seismocardiogram recordings. Evolving Systems 11, 443–452 (2020). https://doi.org/10.1007/s12530-018-9238-8
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DOI: https://doi.org/10.1007/s12530-018-9238-8