Feasibility of estimating tidal volume from electrocardiograph-derived respiration signal and respiration waveform

Estimating tidal volume (V_T) from electrocardiography (ECG) can be quite useful during deep sedation or spinal anesthesia since it eliminates the need for additional monitoring of ventilation. This study aims to validate and compare V_T estimation methodologies based on ECG-derived respiration (EDR) using real-world clinical data.

We analyzed data from 90 critically ill patients for general analysis and two critically ill patients for constrained analysis. EDR signals were generated from ECG data, and V_T was estimated using impedance-based respiration waveforms. Linear regression and deep learning models, both subject-independent and subject-specific, were evaluated using mean absolute error and Pearson correlation.

There was a strong short-term correlation between V_T and the respiration waveform (r = 0.78 and 0.96), which weakened over longer periods (r = 0.23 and - 0.16). V_T prediction models performed poorly in the general population (R² = 0.17) but showed satisfactory performance in two constrained patient records using measured respiration waveforms (R² = 0.84 to 0.94).

Although EDR-based V_T estimation is promising, current methodologies are limited by noisy ICU ECG signals, but controlled environment data showed significant short-term correlations with measured respiration waveforms. Future studies should develop reliable EDR extraction procedures and improve predictive models to broaden clinical applications.