@InCollection{SantosBarGodMacFre:2014:HeRaVa,
               author = "Santos, Laurita dos and Barroso, Joaquim Jos{\'e} and Godoy, 
                         Moacir F. de and Macau, Elbert Einstein Nehrer and Freitas, 
                         Ubiratan S.",
                title = "Recurrence Quantification Analysis as a Tool for Discrimination 
                         Among Different Dynamics Classes: The Heart Rate Variability 
                         Associated to Different Age Groups",
            booktitle = "Proceedings in Mathematics \& Statistics",
            publisher = "Springer International Publishing",
                 year = "2014",
               editor = "Sarkar, S. and Basu, U. and De, S.",
                pages = "125--136",
             keywords = "recurrence quantification analysis, heart rate variability.",
             abstract = "We 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.",
          affiliation = "{Universidade do Vale do Para{\'{\i}}ba (UNIVAP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Faculdade de Medicina 
                         de S{\~a}o Jos{\'e} do Rio Preto (FAMERP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universit{\'e} de 
                         Rouen}",
                  doi = "10.1007/978-3-319-09531-8_8",
                  url = "http://dx.doi.org/10.1007/978-3-319-09531-8_8",
                 isbn = "9783319095301",
                label = "lattes: 5240243263075069 2 SantosBarGodMacFre:2014:HeRaVa",
             language = "en",
           targetfile = "santos_recurrence.pdf",
                  url = "http://link.springer.com/10.1007/978-3-319-09531-8_8",
               volume = "103",
        urlaccessdate = "21 maio 2024"
}