ArticleFrontiers in psychology2026
Analysis of heart rate variability and subtle ECG changes based on machine learning for objective assessment of the psychological state of military personnel.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Heart rate variability, ECG minor changes, and HRV-ECG coupling associated with an adjunctive structured breathing-meditation protocol in combat-exposed military personnel.Frontiers in network physiology · 2026Article
- Human limits in next-generation fighter aviation: psychophysiological stressors, readiness, health, performance, and countermeasures.Frontiers in physiology · 2026Review
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9 authors.
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Abstract
Introduction: The implementation of objective methods for rapid assessment of the psychological and physiological readiness of military personnel is an extremely relevant task. The cardiovascular system acts as a "mirror" of functional and psychological state. The most common and accessible method for the objective study of the cardiovascular system remains electrocardiography (ECG). This study aims to develop a technology for objective monitoring of the psycho-emotional state and overall functional condition of personnel in the Ukrainian Defense Forces using miniature ECG devices and in-depth analysis of ECG signals with artificial intelligence. Methods: Using an innovative ECG device, 90 servicemen, average age of 38 years, undergoing sanatorium treatment and rehabilitation at the Central Military Clinical Sanatorium "Khmilnyk" were examined. The examination was conducted on the first or second day after the start of sanatorium treatment. ECG and HRV analysis were performed using our previously developed Universal Scoring System. The results of ECG analysis from limb leads in 6 leads were compared with 4 well-known psychological self-assessment methods: Beck Anxiety Scale, PCL-5, PHQ-9, as well as a formalized psychologist's conclusion. Correlation analysis and Sequential Feature Selector were used. Results: Forty ECG/HRV features were selected for each of the four psychological methods to find the maximum Conclusion: The study's results demonstrate the potential for significant prediction of routine psychological assessment outcomes based on in-depth analysis of ECG and HRV, especially regarding the Beck Anxiety Scale and Preliminary Psychological Conclusion.
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