Autonomic Indicators For Assessing Heart Rate Variability In Cadets During The Monitoring Of The Educational Process
DOI:
https://doi.org/10.64149/J.Ver.8.18s.385-393Keywords:
Heart Rate Variability, Autonomic Index, Pnn50, Amo, Autonomic Nervous System, Parasympathetic Regulation, Military Cadets, Functional Assessment, Hrv Analysis, Centralization Of Rhythm ControlAbstract
The assessment of heart rate variability (HRV) has emerged as a key method for evaluating the functional state and adaptive capacity of the autonomic nervous system, particularly in high-stress environments such as military training. This article investigates the methodological challenges associated with selecting optimal HRV indicators for monitoring cadets during intensive educational and physical workloads. While numerous HRV parameters exist in both time and frequency domains, their variability under the influence of artifacts, ectopic beats, respiratory rate, and other random events complicates their clinical and practical interpretation. Through a combination of literature review, empirical observation of 130 cardiograms, and statistical correlation analysis, this study proposes a novel, integrative metric–the Autonomic Index (AI)–as a more reliable tool for HRV assessment in military populations.
The AI is calculated from two widely recognized HRV parameters: pNN50, which reflects parasympathetic nervous system activity, and AMo (mode amplitude), which characterizes the degree of centralization in heart rhythm regulation. This new composite index addresses limitations observed in existing indicators by demonstrating low sensitivity to respiratory rate and random fluctuations, while maintaining a strong correlation with other major HRV parameters. Furthermore, its computational simplicity and interpretability make it particularly useful for both real-time functional monitoring and comparative longitudinal studies.
The study also presents a detailed interpretation scale for AI values and discusses its advantages over commonly used individual indicators, especially in conditions of intense physical and psychological stress. It concludes that the AI can serve as a reliable, physiologically meaningful, and operationally convenient measure of autonomic regulation and functional status in cadets. Incorporating this index into routine HRV analysis may enhance the precision of diagnostics and optimize training loads in military education systems.
