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Tipo da ReferênciaBook Section
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Rótulolattes: 5240243263075069 2 SantosBarGodMacFre:2014:HeRaVa
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Chave de CitaçãoSantosBarGodMacFre:2014:HeRaVa
Autor1 Santos, Laurita dos
2 Barroso, Joaquim José
3 Godoy, Moacir F. de
4 Macau, Elbert Einstein Nehrer
5 Freitas, Ubiratan S.
Identificador de Curriculo1
Afiliação1 Universidade do Vale do Paraíba (UNIVAP)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Faculdade de Medicina de São José do Rio Preto (FAMERP)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Université de Rouen
Endereço de e-Mail do Autor1
TítuloRecurrence Quantification Analysis as a Tool for Discrimination Among Different Dynamics Classes: The Heart Rate Variability Associated to Different Age Groups
Título do LivroProceedings in Mathematics & Statistics
Editora (Publisher)Springer International Publishing
Palavras-Chaverecurrence quantification analysis, heart rate variability.
ResumoWe propose a classification method based on recurrence quantification analysis (RQA) combined with support vector machines (SVM). This method combines in an effective way various quantitative descriptors to allow a refined discrimination among dynamical non linear systems that presents dynamics which are very similar to each other. To show how effective this methodology is, firstly, based on synthetic data, it is applied on time series generated from the logistic map with nearby parameter values and in the chaotic regime. Next, it is applied to human biosignals, namely, heart rate variability (HRV) time series obtained from four groups of individuals (premature newborns, full-term newborns, healthy young adults, and adults with severe coronary disease). Roughly the proposed methodology works as follows: The signals are transformed into recurrence plots (RP) and a set of RQA statistical features (recurrence rate, determinism, averaged and maximal diagonal line lengths, entropy, laminarity, trapping time, and length of longest vertical line) are extracted to form the input vector for a SVM classifier. Results show that the method discriminates groups of different ages with classification accuracy better than . Given that heart rate continuously fluctuates over time and reflects different mechanisms to maintain cardiovascular homeostasis of an individual, the results obtained may allow to draw important information on the autonomic control of circulation in normal and diseased conditions.
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Tipo SecundárioPRE LI
Nota TerciáriaTrabalho não Vinculado à Tese/Dissertação
Tamanho939 KiB
Número de Arquivos1
Última Atualização2015: administrator
Última Atualização dos Metadados2018: administrator {D 2014}
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Grupo de Usuárioslattes
Tipo do ConteudoExternal Contribution
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Tipo de Versãopublisher
Permissão de Leituradeny from all and allow from 150.163
Unidades Imediatamente Superiores8JMKD3MGPCW/3ESGTTP
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Histórico2014-12-01 14:46:06 :: lattes -> administrator ::
2014-12-02 10:37:09 :: administrator -> lattes :: 2014
2014-12-09 12:55:22 :: lattes -> administrator :: 2014
2015-01-06 15:53:22 :: administrator -> :: 2014
2015-02-19 11:49:33 :: -> administrator :: 2014
2018-06-04 23:39:45 :: administrator -> :: 2014
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Data de Acesso23 out. 2020