Ideal of Fuzzy Inference System and Manifold Deterioration Using Genetic Algorithm and Particle Swarm Optimization

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Ideal of Fuzzy Inference System and Manifold Deterioration Using Genetic Algorithm and Particle Swarm Optimization is presented. Hypoglycaemia or low blood glucose often occurs with patients that take insulin therapy for diabetes. Hypoglycaemia is serious and causes unconsciousness, seizures or even death. The proposed system uses ECG signal for the detection of hypoglycaemia.

 To find the presence of the hypoglycaemic episodes the system uses heart rate (HR), corrected QT interval, change of HR and change of corrected QT interval of the ECG signal. The system is developed using multiple regression with fuzzy inference system (FIS). Genetic algorithm and particle swarm optimization is used to optimize the parameters of FIS and multiple regressions. Fuzzy Inference System is used to estimate the hypo level based on the physiological parameters. The physiological parameters are heart rate and corrected QT interval.

Multiple regressions are used to fine tune the performance of the hypoglycaemic detection based on the estimated hypo level and the change of the HR and corrected QT interval. Thus, estimate the presence of hypoglycaemia using the FIS and multiple regressions with genetic algorithm and also with particle swarm optimation and finally comparing the performance of both techniques

Multiple regressions with FIS detection algorithm are developed to recognize the presence of hypoglycaemic episodes. The aforementioned results indicate that hypoglycaemia can be detected noninvasively, continuously, and effectively from the real-time physiological responses. A multiple regression with FIS is proposed to detect the presence of hypoglycaemic episodes. To optimize the fuzzy rules and the regression model, genetic algorithm and particle swarm optimization can be used. Finally, the performance of the system with genetic algorithm and the system with particle swarm optimization are compared.

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