This study conducted comprehensive validation of the mathematical model of the discrete amplitude variability function for characteristic electrocardiographic wave peaks using clinical data from patients with myocardial ischemia, supraventricular arrhythmia, ventricular tachycardia, and normal sinus rhythm. The research investigated the stationarity of the amplitude variability function using the Augmented Dickey-Fuller test and distribution normality using the Anderson-Darling test on electrocardiogram recordings from the PhysioNet database. Applied statistical analysis included moments calculation encompassing mean, variance, skewness, and kurtosis to characterize pathology-specific patterns. The mathematical model was formalized as the discrete function ()()(1)kkkVmAmAm=−−, where ()kAm represents the amplitude of the k-th type peak in the m-th cardiac cycle and (1)kAm− represents the amplitude in the previous valid cardiac cycle. Statistical testing was performed at significance level α = 0.05 for stationarity assessment with null hypothesis of unit root presence, and critical value A2 = 0.712 for normality verification. Results established stationarity of the variability function for all diagnostic categories with p-values of 0.0111 for myocardial ischemia, less than 0.0001 for supraventricular arrhythmia, 0.0061 for ventricular tachycardia, and 0.0031 for normal sinus rhythm, confirming the absence of systematic temporal trends and validating the fundamental correctness of the proposed mathematical model. Normality testing demonstrated that 75 % of cases showed correspondence to normal distribution, with Anderson-Darling statistics of 0.5288 for myocardial ischemia, 0.1634 for supraventricular arrhythmia, 0.3256 for normal sinus rhythm, while ventricular tachycardia showed deviation with A2=0.8835. Statistical parameters revealed pathology-specific characteristics: ventricular tachycardia exhibited maximum skewness (γ1=1.4334) and kurtosis (γ2=2.7315) indicating heavy-tailed distribution and episodic amplitude variations, while normal sinus rhythm showed maximum variance (σ2=0.0674 mV2) reflecting natural heart rate variability. The validated model enables integration into automated ECG diagnostic systems for cardiovascular disease detection, providing theoretical foundation for machine learning algorithms development. Practical significance includes potential application of identified statistical markers in clinical practice for differential arrhythmia diagnosis. Future research should expand the analysis to other electrocardiographic wave components including P, Q, S, and T waves, and incorporate larger patient populations to enhance statistical power and clinical applicability of the findings.
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