Time-based pulmonary features from electrical impedance tomography demonstrate ventilation heterogeneity in chronic obstructive pulmonary disease
Bibliographic record
Abstract
Pulmonary electrical impedance tomography (EIT) is a functional imaging technique that allows real-time monitoring of ventilation distribution. Ventilation heterogeneity (VH) is a characteristic feature of chronic obstructive pulmonary disease (COPD) and has previously been quantified using features derived from tidal variations in the amplitude of the EIT signal. However, VH may be better described by time-based metrics, the measurement of which is made possible by the high temporal resolution of EIT. We aimed 1) to quantify VH using novel time-based EIT metrics and 2) to determine the physiological relevance of these metrics by exploring their relationships with complex lung mechanics measured by the forced oscillation technique (FOT). We performed FOT, spirometry, and tidal-breathing EIT measurements in 11 healthy controls and 9 volunteers with COPD. Through offline signal processing, we derived 3 features from the impedance-time ( Z- t) curve for each image pixel: 1) t E , mean expiratory time; 2) PHASE, mean time difference between pixel and global Z- t curves; and 3) AMP, mean amplitude of Z- t curve tidal variation. Distribution was quantified by the coefficient of variation (CV) and the heterogeneity index (HI). Both CV and HI of the t E and PHASE features were significantly increased in COPD compared with controls, and both related to spirometry and FOT resistance and reactance measurements. In contrast, distribution of the AMP feature showed no relationships with lung mechanics. These novel time-based EIT metrics of VH reflect complex lung mechanics in COPD and have the potential to allow real-time visualization of pulmonary physiology in spontaneously breathing subjects. NEW & NOTEWORTHY Pulmonary electrical impedance tomography (EIT) is a real-time imaging technique capable of monitoring ventilation with exquisite temporal resolution. We report novel, time-based EIT measurements that not only demonstrate ventilation heterogeneity in chronic obstructive pulmonary disease (COPD), but also reflect oscillatory lung mechanics. These EIT measurements are noninvasive, radiation-free, easy to obtain, and provide real-time visualization of the complex pathophysiology of COPD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".