Machine learning analysis of ECG and HRV data predicted Beck Anxiety Scale scores with a cross-validated R2 of 0.52 on the training set and 0.36 on the test set in military personnel undergoing rehabilitation.
Observational (n=90)
No
Does machine learning-based analysis of ECG and HRV predict psychological state in military personnel?
90 military personnel (servicemen), average age 38 years, undergoing sanatorium treatment for mild or moderate musculoskeletal diseases, with prior combat zone exposure of at least 3 months.
In-depth analysis of ECG and HRV signals using a miniature ECG device and machine learning (Universal Scoring System)
Correlation between ECG/HRV features and psychological assessment methods (Beck Anxiety Scale, PCL-5, PHQ-9, and formalized psychologist's conclusion)
Machine learning analysis of subtle ECG changes and heart rate variability can provide an objective assessment of psycho-emotional states, particularly anxiety, in military personnel.
Effect estimate: cross-validated R2 training score 0.520 on Beck Anxiety Scale prediction model
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 R 2 metric. The highest number of reliable correlations between ECG and HRV parameters and psychological tests was found for the Beck Anxiety Scale. The same can be said when using feature selection via machine learning. The cross-validated R 2 scores for the training and test sets in the case of the Beck Anxiety Scale were 0.520/0.359, respectively. Similar results were obtained for the Preliminary Psychological. 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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Illya Chaikovsky
V.M. Glushkov Institute of Cybernetics
Ivan Senko
V.M. Glushkov Institute of Cybernetics
Mykola Budnyk
V.M. Glushkov Institute of Cybernetics
Frontiers in Psychology
SHILAP Revista de lepidopterología
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Lesya Ukrainka Volyn National University
V.M. Glushkov Institute of Cybernetics
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Chaikovsky et al. (Fri,) conducted a observational in Military personnel undergoing sanatorium treatment for musculoskeletal diseases of mild or moderate severity with previous combat zone exposure (n=90). ECG and HRV analysis using innovative miniature ECG device and machine learning-based Universal Scoring System vs. Psychological self-assessments (Beck Anxiety Scale, PCL-5, PHQ-9, formal psychologist conclusion) was evaluated on Prediction of psychological assessment outcomes based on ECG/HRV parameters measured by machine learning models (cross-validated R2 training score 0.520 on Beck Anxiety Scale prediction model). Machine learning analysis of ECG and HRV data predicted Beck Anxiety Scale scores with a cross-validated R2 of 0.52 on the training set and 0.36 on the test set in military personnel undergoing rehabilitation.
synapsesocial.com/papers/69a528b3f1e85e5c73bf040c — DOI: https://doi.org/10.3389/fpsyg.2026.1688230