Applying the efunn evolving paradigm to the recognition of artefactual beats in continuous seismocardiogram recordings

Mario Malcangi, Hao Quan, Emanuele Vaini, Prospero Lombardi, Marco Di Rienzo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Seismocardiogram (SCG) recording is a novel method for the prolonged monitoring of the cardiac mechanical performance during spontaneous behavior. The continuous monitoring results in a collection of thousands of beats 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. On this premise, we propose the use of the Evolving Fuzzy Neural Network (EFuNN) paradigm for the automatic artifact detection 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. Preliminary tests have been carried out to validate this approach and the obtained results demonstrate the effectiveness of the method and its scalability.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 18th International Conference, EANN 2017, Proceedings
PublisherSpringer Verlag
Pages256-264
Number of pages9
Volume744
ISBN (Print)9783319651712
DOIs
Publication statusPublished - 2017
Event18th International Conference on Engineering Applications of Neural Networks, EANN 2017 - Athens, Greece
Duration: Aug 25 2017Aug 27 2017

Publication series

NameCommunications in Computer and Information Science
Volume744
ISSN (Print)1865-0929

Conference

Conference18th International Conference on Engineering Applications of Neural Networks, EANN 2017
CountryGreece
CityAthens
Period8/25/178/27/17

Keywords

  • Artfact identification
  • Evolving Fuzzy Neural Network
  • Seismocardiogram

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Malcangi, M., Quan, H., Vaini, E., Lombardi, P., & Di Rienzo, M. (2017). Applying the efunn evolving paradigm to the recognition of artefactual beats in continuous seismocardiogram recordings. In Engineering Applications of Neural Networks - 18th International Conference, EANN 2017, Proceedings (Vol. 744, pp. 256-264). (Communications in Computer and Information Science; Vol. 744). Springer Verlag. https://doi.org/10.1007/978-3-319-65172-9_22