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This paper describes a self-tuning method of membership functions in a fuzzy-logic-based cardiac pacing system and validates its feasibility in a double sensor system which has minute ventilation and oxygen saturation level as its guides for the rate regulation. Though the agreement between the pacing rates (fuzzy rates) calculated with three linguistic variables for each parameter and the target rates were not satisfactory, it was improved significantly by tuning the membership functions. Almost the same evaluated values with those obtained by using six linguistic variables for each parameter were obtained. Time required for the self-tuning process was about 40 s (386CPU, 20 MHz) which was fast enough for the system. The smaller number of linguistic labels results in a smaller number of rules, which is beneficial in implantable cardiac pacemakers with limited memory capacity. A fuzzy-logic-based cardiac pacing system is promising for the realization of custom-made cardiac pacemakers.

A method of retrieving a temperature-depth profile in biological object from a set of multi-frequency microwave radiometric data has been developed. The method is a combination of model-fitting and Monte Carlo techniques and is capable of estimating a profile and its confidence interval as a function of the depth. We use 2σ -intervals as a measure of the precision of tissue temperature measurements. The method was tested and supported by an experiment in which temperature distributions in a muscle equivalent agar phantom were measured using a 5-band, 1-3.8 GHz radiometer with the brightness temperature resolution of 0.05-0.07 K. A typical result of the experiment showed that 2σ -intervals were 1 K or less for 0 < z < 3 cm, 1.4 K at z = 4 cm, and 3 K at z = 5 cm. A numerical simulation study was made using this technique to assess effects of the selection of measurement frequencies, number of frequency bands, brightness temperature resolution of radiometer and thickness of fat layer on the precision. Resul...