MATHEMATICAL MODEL OF EXTREMA VARIATIONS IN CHARACTERISTIC WAVES OF ELECTROCARDIOGRAPHIC SIGNALS BASED ON A DISCRETE FUNCTION OF AMPLITUDE VARIABILITY

Abstract

The article presents a mathematical model of extrema variations of characteristic waves of electrocardiographic signals based on a discrete function of amplitude variability, which allows detecting signs of cardiovascular pathologies. The proposed model takes into account the amplitude values of the characteristic ECG waves (P, Q, R, S, and T) and their changes between consecutive cardiac cycles. The function of amplitude variability is defined as the difference between the amplitudes of the corresponding teeth in the current and previous cardiac cycles, which allows quantifying the instability of the amplitude characteristics. 
 The diagnostic value of the proposed model is demonstrated through the analysis of electrocardiogram signals from patients with conditional norm and extrasystole. It was found that during normal functioning of the cardiovascular system, the amplitude variability function is characterized by lower variability and amplitude, whereas in pathological conditions, significant deviations and irregularity of indicators are observed. The method provides reliable differentiation between normal and pathological cardiac states, with particularly pronounced differences in the amplitude variability patterns of R and T waves during extrasystole compared to normal cardiac function. 
 The developed method can be used for automated cardiac diagnostics, which will facilitate timely treatment and prevention of cardiovascular diseases. The amplitude variability function offers new diagnostic features that complement traditional ECS analysis by focusing on dynamic changes in wave amplitudes during cardiac cycles rather than static morphological features. This approach opens up new possibilities for predictive modeling in cardiology and can be integrated into existing diagnostic systems to increase their sensitivity and specificity for detecting cardiac disorders before they develop into more severe conditions.