(untitled) (Computer systems and information technologies, 2025)
This paper presents an information technology for electrocardiographic signal analysis based on discrete mathematical models with temporal rhythm functions and amplitude variability of characteristic waves P, Q, R, S, T. A discrete mathematical model of the temporal rhythm function considering extreme amplitude values of ECS characteristic waves and an amplitude variability model have been developed for comprehensive analysis of morphological and rhythmic diagnostic features of cardiac signals. Experimental validation was conducted on ECS signals from patients with diagnoses: conditional norm and extrasystole. For patients with conditional norm, high stability of temporal intervals between ECS characteristic waves is observed with a mathematical expectation of 0.776 s for all wave types and minimal amplitude variability (mathematical expectation 0.00003-0.00064 mV, variance 0.00010-0.00022 mV). In patients with extrasystole, significant cardiac rhythm irregularity was detected with a decrease in mathematical expectation to 0.503-0.504 s (by 35%) and a three-order magnitude increase in variance (to 0.011-0.012 s) for temporal rhythm functions. The amplitude variability function demonstrated exponential growth of all statistical parameters: mathematical expectation increased to 0.070-0.452 mV (from 233 to 15067 times), variance reached extreme values of 78.44-719.20 mV (5-6 order magnitude increase), range varied within 46.2-122.9 mV (960 to 1500 times increase). The proposed discrete mathematical models successfully combine temporal rhythm functions considering extreme amplitude values of ECS characteristic waves with amplitude variability functions, enabling comprehensive assessment of both rhythmic and morphological ECS features. The models demonstrate high sensitivity to pathological changes in the cardiovascular system and expand the methodological foundation for developing information technology for expert analysis of morphological and rhythmic features of cardiac signals through integration with machine learning and artificial intelligence methods.