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A mixture of fuzzy filters applied to the analysis of heartbeat intervals

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Abstract

This study provides a stochastic modeling of the heartbeat intervals using a mixture of Takagi–Sugeno type fuzzy filters. The model parameters are inferred under variational Bayes (VB) framework. The model of the heartbeat intervals is in the form of a history-dependent probability density. The parameters, characterizing the heartbeat intervals probability density, include the estimated parameters of different fuzzy filters and may serve as the features of the heartbeat interval series. The features of the heartbeat intervals provide a description of the physiological state of an individual. A novelty of our analysis method is that the physiological state is predicted as a part of the features extraction procedure. This is done via deriving, using VB paradigm, an analytical expression for the posterior distribution that the observed heartbeat intervals have been generated by the stochastic model of the physiological state. The method is illustrated with the data of 40 healthy subjects studied in a tilt-table experiment.

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Authors and Affiliations

  1. Center for Life Science Automation, F.-Barnewitz-Str. 8, 18119, Rostock, Germany

    Mohit Kumar

  2. Institute of Preventive Medicine, St.-Georg-Str. 108, 18055, Rostock, Germany

    Matthias Weippert & Regina Stoll

  3. Institute of Automation, Richard-Wagner-Str. 31, 18119, Rostock, Germany

    Norbert Stoll

Authors
  1. Mohit Kumar
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  2. Matthias Weippert
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  3. Norbert Stoll
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  4. Regina Stoll
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Corresponding author

Correspondence to Mohit Kumar.

Additional information

This work was supported by Center for Life Science Automation, Rostock, Germany.

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Kumar, M., Weippert, M., Stoll, N. et al. A mixture of fuzzy filters applied to the analysis of heartbeat intervals. Fuzzy Optim Decis Making 9, 383–412 (2010). https://doi.org/10.1007/s10700-010-9089-7

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  • DOI: https://doi.org/10.1007/s10700-010-9089-7

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