Intelligent Fuzzy Computing for Physiological Signal Analysis
Fuzzy Filtering for Physiological Signal Analysis
This study suggests the use of fuzzy filtering algorithms to deal with the uncertainties associated to the interpretation of analysis of physiological signals. The signal characteristics, for a given situation or physiological state, vary for an individual over time and also vary among the individuals with the same state. These random variations are due to the several time-varying factors related to the physiological behavior of individuals which can't be taken into account in the interpretation of signal characteristics for solving a medical decision making problem. The approach is to reduce the effect of random variations on the analysis of signal characteristics via filtering out randomness or uncertainty from the signal using a nonlinear fuzzy filter.
A Mixture of Fuzzy Filters Applied to the Analysis of Heartbeat Intervals
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.
Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals
Quantifying stress levels of an individual based on a mathematical analysis of real-time physiological data measurements is challenging. This study suggests a stochastic fuzzy analysis method to evaluate the short time series of R-R intervals for a quantification of the stress level. The five minutes long series of R-R intervals recorded under a given stress level are modeled by a stochastic fuzzy system. The stochastic model of heartbeat intervals is individual specific and corresponds to a particular stress level. Once the different heartbeat intervals models are available for an individual, an analysis of the given R-R interval series generated under an unknown stress level is performed by a stochastic interpolation of the models. The stress estimation method has been implemented in a mobile telemedical application employing an e-health system for an efficient and cost-effective monitoring of patients while being at home or practicing their daily jobs. The experiments involve 50 individuals whose stress scores were assessed at different times of the day. The subjective rating scores showed a high correlation with the values predicted by proposed analysis method.
- M. Kumar, M. Weippert, D. Arndt, S. Kreuzfeld, K. Thurow, N. Stoll, R. Stoll, “Fuzzy Filtering for Physiological Signal Analysis,” IEEE Transactions Fuzzy Systems, vol. 18, no. 1, pp. 208-216, 2010.
- M. Kumar, M. Weippert, N. Stoll, and R. Stoll, “A Mixture of Fuzzy Filters Applied to the Analysis of Heart Beat Intervals,” Fuzzy Optimization and Decision Making, vol. 9, no. 4, pp. 383-412, 2010.
- M. Kumar, S. Neubert, S. Behrendt, A. Rieger, M. Weippert, N. Stoll, K. Thurow, and R. Stoll, “Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 4, pp. 746-759, 2012.