Robust Solution to Fuzzy Identification Problem with Uncertain Data by Regularization
Fuzzy Optimization and Decision Making
Robust Adaptive Identification of Fuzzy Systems with Uncertain Data
Fuzzy Optimization and Decision Making
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
International Journal of Human-Computer Studies
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Fuzzy filtering for physiological signal analysis
IEEE Transactions on Fuzzy Systems
Variational bayes for a mixed stochastic/deterministic fuzzy filter
IEEE Transactions on Fuzzy Systems
Fuzzy Techniques for Subjective Workload-Score Modeling Under Uncertainties
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
TSK-fuzzy modeling based on ϵ-insensitive learning
IEEE Transactions on Fuzzy Systems
Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment
IEEE Transactions on Fuzzy Systems
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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.