Analysis of heart rate variability and subtle ECG changes based on machine learning for objective assessment of the psychological state of military personnel

Illya Chaikovsky; Ivan Senko; Mykola Budnyk; Viktor Matsyshyn; Tetiana Ryzhenko; Vitaliy Budnyk; Oleksandr Romanchuk; Anton Popov; Petro Stetsyuk

doi:10.3389/fpsyg.2026.1688230

Analysis of heart rate variability and subtle ECG changes based on machine learning for objective assessment of the psychological state of military personnel

Front Psychol. 2026 Feb 27:17:1688230. doi: 10.3389/fpsyg.2026.1688230. eCollection 2026.

Authors

Affiliations

¹ Department of Contactless Control Systems, V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, Kyiv, Ukraine.
² Department of State Administrative, State Institution of Science «Center of Innovative Healthcare Technologies», Kyiv, Ukraine.
³ Department of Internal and Family Medicine, Lesya Ukrainka Volyn National University, Lutsk, Ukraine.
⁴ Department of Therapy and Rehabilitation, Ivan Bobersky Lviv State University of Physical Culture, Lviv, Ukraine.
⁵ Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine.
⁶ Faculty of Applied Sciences, Ukrainian Catholic University, Lviv, Ukraine.

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 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.

Keywords: ECG; correlation analysis; heart rate variability; machine learning; military personnel; psychological questionnaires; signal analysis.