Filtros : "Biomedical Signal Processing and Control" Removidos: "Fazan Júnior, Rubens" "Gotoh, Toshiyuki" Limpar

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  • Source: Biomedical Signal Processing and Control. Unidades: IFSC, ICMC

    Subjects: REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, TECNOLOGIAS DA SAÚDE, OSTEOARTRITE DO JOELHO

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    • ABNT

      RIBAS, Lucas Correia et al. A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative. Biomedical Signal Processing and Control, v. 222, n. Ja 2022, p. 103133-1-103133-10, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.bspc.2021.103133. Acesso em: 03 nov. 2024.
    • APA

      Ribas, L. C., Riad, R., Jennane, R., & Bruno, O. M. (2022). A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative. Biomedical Signal Processing and Control, 222( Ja 2022), 103133-1-103133-10. doi:10.1016/j.bspc.2021.103133
    • NLM

      Ribas LC, Riad R, Jennane R, Bruno OM. A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative [Internet]. Biomedical Signal Processing and Control. 2022 ; 222( Ja 2022): 103133-1-103133-10.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.bspc.2021.103133
    • Vancouver

      Ribas LC, Riad R, Jennane R, Bruno OM. A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative [Internet]. Biomedical Signal Processing and Control. 2022 ; 222( Ja 2022): 103133-1-103133-10.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.bspc.2021.103133
  • Source: Biomedical Signal Processing and Control. Unidades: EEFERP, FMRP, EEFE

    Subjects: MÚSCULOS, ELETROMIOGRAFIA, HABILIDADES MOTORAS

    Acesso à fonteDOIHow to cite
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    • ABNT

      PACHECO, Matheus Maia et al. Convergence in myoelectric control: Between individual patterns of myoelectric learning. Biomedical Signal Processing and Control, v. 70, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.bspc.2021.103057. Acesso em: 03 nov. 2024.
    • APA

      Pacheco, M. M., Moraes, R. de, Lemos, T. W. de, Bongers, R. M., & Tani, G. (2021). Convergence in myoelectric control: Between individual patterns of myoelectric learning. Biomedical Signal Processing and Control, 70. doi:10.1016/j.bspc.2021.103057
    • NLM

      Pacheco MM, Moraes R de, Lemos TW de, Bongers RM, Tani G. Convergence in myoelectric control: Between individual patterns of myoelectric learning [Internet]. Biomedical Signal Processing and Control. 2021 ; 70[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.bspc.2021.103057
    • Vancouver

      Pacheco MM, Moraes R de, Lemos TW de, Bongers RM, Tani G. Convergence in myoelectric control: Between individual patterns of myoelectric learning [Internet]. Biomedical Signal Processing and Control. 2021 ; 70[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.bspc.2021.103057
  • Source: Biomedical Signal Processing and Control. Unidade: ICMC

    Subjects: PROCESSAMENTO DE SINAIS BIOMÉDICOS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ELETROENCEFALOGRAFIA, EPILEPSIA, TECNOLOGIAS DA SAÚDE

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    • ABNT

      OLIVA, Jefferson Tales e ROSA, João Luís Garcia. Binary and multiclass classifiers based on multitaper spectral features for epilepsy detection. Biomedical Signal Processing and Control, v. 66, p. 1-16, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.bspc.2021.102469. Acesso em: 03 nov. 2024.
    • APA

      Oliva, J. T., & Rosa, J. L. G. (2021). Binary and multiclass classifiers based on multitaper spectral features for epilepsy detection. Biomedical Signal Processing and Control, 66, 1-16. doi:10.1016/j.bspc.2021.102469
    • NLM

      Oliva JT, Rosa JLG. Binary and multiclass classifiers based on multitaper spectral features for epilepsy detection [Internet]. Biomedical Signal Processing and Control. 2021 ; 66 1-16.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.bspc.2021.102469
    • Vancouver

      Oliva JT, Rosa JLG. Binary and multiclass classifiers based on multitaper spectral features for epilepsy detection [Internet]. Biomedical Signal Processing and Control. 2021 ; 66 1-16.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.bspc.2021.102469
  • Source: Biomedical Signal Processing and Control. Unidade: IB

    Subjects: FREQUÊNCIA CARDÍACA, SISTEMA NERVOSO SIMPÁTICO, FISIOLOGIA CARDIOVASCULAR

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    • ABNT

      NATALI, José Eduardo Soubhia e STARZYNSKI, Paulo Nogueira e CHAUI-BERLINCK, Jose Guilherme. Oscillatory patterns in heart rate variability and complexity: a meta-analysis. Biomedical Signal Processing and Control, v. 33, p. 66-71, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.bspc.2016.11.012. Acesso em: 03 nov. 2024.
    • APA

      Natali, J. E. S., Starzynski, P. N., & Chaui-Berlinck, J. G. (2016). Oscillatory patterns in heart rate variability and complexity: a meta-analysis. Biomedical Signal Processing and Control, 33, 66-71. doi:10.1016/j.bspc.2016.11.012
    • NLM

      Natali JES, Starzynski PN, Chaui-Berlinck JG. Oscillatory patterns in heart rate variability and complexity: a meta-analysis [Internet]. Biomedical Signal Processing and Control. 2016 ; 33 66-71.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.bspc.2016.11.012
    • Vancouver

      Natali JES, Starzynski PN, Chaui-Berlinck JG. Oscillatory patterns in heart rate variability and complexity: a meta-analysis [Internet]. Biomedical Signal Processing and Control. 2016 ; 33 66-71.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.bspc.2016.11.012

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