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Bayesian hierarchical model with wavelet transform coefficients of the ECG in obstructive sleep apnea screening

Abstract:

Wavelet Transform allows analyzing the properties of a variety of signals, being able to emphasize changes in either time or frequency domain once the appropriate scale in chosen. Since a signal can be expressed in terms of coefficients from wavelet functions, the behavior of this signal could be sparsely represented in these functions, expressing possible properties behind nonstationary signals. Recently, methods based on hierarchical Bayes analysis have been found to be a feasible tool in the approach of physical science and engineering applications. In order to participate in the apnea screening event at the Computers in Cardiology Challenge 2000 and estimate a model that could bring us to an adequate classification between groups we developed the present methodology.

Año de publicación:

2000

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Estadísticas
    • Modelo matemático
    • Aprendizaje automático

    Áreas temáticas:

    • Fisiología humana
    • Representaciones escénicas
    • Enfermedades

    Contribuidores:

    Passariello G.Passariello G.
    A. La CruzA. La Cruz
    Ng F.Ng F.
    Gomis P.Gomis P.
    García I.García I.
    Mora F.Mora F.