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The aim of this study was to develop predictive models for assessing the influence of respiratory pattern on sympathovagal balance, expressed by the LF/HF ratio of heart rate variability (HRV). The research was carried at the Department of Human Physiology and Biophysics, using data obtained from the analysis of seven experimental breathing models. Respiratory parameters were recorded by inductance respiratory plethysmography (IRP), using the “Visuresp” and “CapnoStream20” systems, and HRV analysis of the ECG signal, recorded using BIOPAC Systems MP100, was performed using the “Kubios HRV Standard” program. In statistical processing, multivariate analysis with the Backward variable selection method was applied, using the IBM SPSS Statistics 26.0, to identify respiratory parameters with significant predictive value. The results showed that respiratory minute volume (RMV) and total respiratory cycle duration (Tt), measured under resting breathing conditions, have an important predictive value in estimating sympathovagal balance in abdominal (AR) and controlled breathing (R5/5) patterns. Reducing RMV and extending the duration of the respiratory cycle through conscious control of the respiratory pattern favored the increase of parasympathetic influence and the decrease of sympathetic activity. The conclusions support the use of breathing regulation techniques, especially abdominal and controlled breathing, as effective non-pharmacological methods for optimizing autonomic function, with applicability in health promotion and prevention of disorders associated with autonomic imbalance.

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Predictive Models of Sympathovagal Balance Based on the Parameters of Controlled Breathing Pattern

  • 会议论文

  • 发表日期：

    2025年

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  • Ina Timotin,
  • Svetlana Lozovanu,
  • Andrei Ganenco,
  • Aliona Dobrovolskaia,
  • Adrian Lupusor,
  • Victor Ojog

摘要

The aim of this study was to develop predictive models for assessing the influence of respiratory pattern on sympathovagal balance, expressed by the LF/HF ratio of heart rate variability (HRV). The research was carried at the Department of Human Physiology and Biophysics, using data obtained from the analysis of seven experimental breathing models. Respiratory parameters were recorded by inductance respiratory plethysmography (IRP), using the “Visuresp” and “CapnoStream20” systems, and HRV analysis of the ECG signal, recorded using BIOPAC Systems MP100, was performed using the “Kubios HRV Standard” program. In statistical processing, multivariate analysis with the Backward variable selection method was applied, using the IBM SPSS Statistics 26.0, to identify respiratory parameters with significant predictive value. The results showed that respiratory minute volume (RMV) and total respiratory cycle duration (Tt), measured under resting breathing conditions, have an important predictive value in estimating sympathovagal balance in abdominal (AR) and controlled breathing (R5/5) patterns. Reducing RMV and extending the duration of the respiratory cycle through conscious control of the respiratory pattern favored the increase of parasympathetic influence and the decrease of sympathetic activity. The conclusions support the use of breathing regulation techniques, especially abdominal and controlled breathing, as effective non-pharmacological methods for optimizing autonomic function, with applicability in health promotion and prevention of disorders associated with autonomic imbalance.