Fuzzy controller design for predicting the systolic area of photoplethysmogram signal using Persian medicine pulsology

BACKGROUND: Photoplethysmography, a method used to measure blood volume changes per pulse, is widely applied in healthcare. In Persian medicine, pulsology is considered one of the most important methods for clinical diagnosis. However, recently the theory of fuzzy sets has provided a valuable foundation for developing knowledge-based systems in medical research. AIM: To estimate and predict the systolic area of photoplethysmography signals using Persian medicine pulsology, by leveraging the potential of fuzzy systems. METHODS: To design the fuzzy controller, a Persian medicine specialist simultaneously recorded data on PM pulse characteristics including pulse frequency and pulse strength—along with photoplethysmography signals, from 55 healthy volunteers. Initially, rules were generated based on the input and output variables. After evaluating these rules using the collected data, 35 were retained and presented in a two-input–one-output lookup table. RESULTS: The fuzzy system was then constructed using MATLAB. It included 35 rules, triangular and trapezoidal membership functions, a singleton fuzzifier, a product inference engine, and a center-average defuzzifier. This system, which used pulse frequency and pulse strength as inputs and provided the systolic area as output, demonstrated acceptable performance within the defined input range. CONCLUSIONS: The proposed fuzzy controller system reasonably predicted the systolic area of photoplethysmography signals using Persian medicine pulse parameters. The results revealed that increasing pulse frequency decreased the systolic area, while increasing pulse strength increased it, in alignment with previous results. Therefore, this system may boost the clinical skills of Persian medicine students and practitioners. It also holds promise for application in disease diagnosis and prediction and for facilitating integration between Persian medicine and mainstream medicine.