A self-tuning effect of membership functions in a fuzzy-logic-based cardiac pacing system

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.