The Utility of the TKEO Parameter in the assessment of thoracic respiratory movements

Introduction Chronic obstructive pulmonary disease (COPD) and dysphonia are prevalent disorders associated with altered respiratory patterns, posing significant diagnostic and therapeutic challenges. Accurate and efficient assessment of thoracic respiratory movements is essential for individualized therapy and monitoring. The Teager-Kaiser Energy Operator (TKEO), originally developed for speech analysis, offers a single-parameter approach to evaluate both amplitude and frequency characteristics of biosignals. This study aimed to determine the utility of TKEO in assessing thoracic respiratory patterns in healthy individuals, COPD patients, and those with dysphonia. Material and methods Sixty-one participants were enrolled: 30 healthy controls, 16 with COPD, and 15 with dysphonia. Thoracic and abdominal respiratory movements were recorded using Respiratory Inductive Plethysmography (RIP) in both standing and seated positions. The TKEO parameter was calculated from the displacement signals, and its values were compared with conventional respiratory parameters (inspiratory and expiratory depths) obtained via RIP. Statistical analysis included the Kruskal-Wallis test and Spearman’s rank correlation to assess group differences and parameter relationships. Results In the seated position, TKEO values and conventional respiratory parameters significantly differentiated between healthy subjects, COPD, and dysphonia groups (p < 0.01). Healthy and COPD subjects exhibited higher diaphragmatic activity, while dysphonia patients showed a dominant upper rib pattern. TKEO values closely paralleled traditional respiratory measures, with strong to very strong positive correlations observed across all groups and positions (Spearman’s ρ, p < 0.001). In the standing position, group differences were less pronounced, but TKEO still reflected underlying respiratory patterns. Conclusions TKEO is a sensitive and efficient parameter for assessing thoracic respiratory movements, correlating strongly with established respiratory metrics. Its application may accelerate and refine the diagnosis of respiratory pattern disorders in clinical practice. Further research should explore TKEO’s integration with artificial intelligence-based diagnostic tools and its utility in larger, age-matched cohorts.