基于波斯医学脉诊的光电容积描记信号预测外周血管阻力的Mamdani与Takagi-Sugeno模糊系统比较评价OA

Objective To perform a comparative evaluation of Mamdani and Takagi-Sugeno(TS)fuzzy inference systems for predicting peripheral vascular resistance from photoplethysmogram(PPG)signals,incorporating diagnostic parameters from Persian medicine(PM)pulsology. Methods Both fuzzy inference systems were implemented in MATLAB R2021b and validated using leave-one-out cross-validation(LOOCV)on clinical PM pulse diagnostic data collected from 35 healthy volunteers.The dataset included pulse frequency scale(1-6)and weakness scale(1-4)alongside corresponding PPG-derived peripheral vascular resistance indices(range:0.019-0.983).The Mamdani system(with 35 rules)was designed using trapezoidal and triangular membership functions,a singleton fuzzifier,a product inference engine,and a centroid defuzzifier.The TS system(with 6 rules)was configured as a first-order model with Gaussian input membership functions.System performance was quantitatively evaluated us-ing mean absolute error(MAE),root mean square error(RMSE),and the coefficient of deter-mination(R2).Statistical significance of performance differences was assessed using a paired t test. Results The Mamdani inference system showed much higher prediction accuracy than the TS system.Comparative analysis revealed a substantial advantage for the Mamdani system(MAE=0.007 63±0.001 20,RMSE=0.008 58±0.001 50,R2=0.998 40±0.000 80)over the TS system(MAE=0.015 40±0.002 10,RMSE=0.022 48±0.002 80,R2=0.989 20±0.001 50).A paired t test comparing the absolute errors of the 35 LOOCV folds confirmed statistical signifi-cance[t(34)=5.07,P<0.001]. Conclusion The findings suggest the potential suitability of the Mamdani system for certain precision-oriented analytical tasks.Both systems exhibit practical utility for integrative medicine diagnostics and wearable health-monitoring applications for healthy individuals,enabling a modern computational translation of PM pulsology.