Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review.

Journal: Biomedical engineering online
PMID: 34130692

Abstract

INTRODUCTION: The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease.

Authors

  • Felipe Fernandes
    Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil. felipe.ricardo@lais.huol.ufrn.br.
  • Ingridy Barbalho
    Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil.
  • Daniele Barros
    Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil.
  • Ricardo Valentim
    Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil.
  • César Teixeira
    Center for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
  • Jorge Henriques
    Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal.
  • Paulo Gil
    Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal.
  • Mário Dourado Júnior
    Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil.

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