BackgroundElectrical impedance tomography (EIT) provides information on global and regional ventilation during tidal breathing and mechanical ventilation. During forced expiration maneuvers, the linearity of EIT and spirometric data has been documented in healthy persons. The present study investigates the potential diagnostic use of EIT in pediatric patients with asthma. MethodsEIT and spirometry were performed in 58 children with asthma (average ageSD: 11.863.13 years), and 58 healthy controls (average age +/- SD: 12.12 +/- 2.9 years). The correlation between EIT data and simultaneously acquired spirometric data were tested for FEV1, FEV0.5, MEF75, MEF50, and MEF25. Binary classification tests were performed for the EIT-derived Tiffeneau index FEV1/FVC and the bronchodilator test index FEV1. Average flow-volume (FV) loops were generated for patients with pathologic spirometry to demonstrate the feasibility of EIT for graphic diagnosis of asthma. ResultsSpirometry and global EIT-based FV loops showed a strong correlation (P<0.001, r>0.9 in FEV1 and FEV0.5). In all criteria, the binary classification tests yielded high specificity (>93%), a high positive predictive value (75%) and a high negative predictive value (>80%), while sensitivity was higher in FEV1 (86.67%) and lower in FEV1/FVC (25% and 35.29%). A typical concave shape of the EIT-derived average FV loops was observed for asthmatic children with improvement after bronchospasmolysis. ConclusionsGlobal FV loops derived from EIT correlate well with spirometry. Positive bronchospasmolysis can be observed in EIT-derived FV loops. Flow-volume loops originated from EIT have a potential to visualize pulmonary function.

This paper describes a novel extension of the Forced Oscillation Technique (FOT), namely the volume-dependent Forced Oscillation Technique (v-FOT). This extension is based on the measurement of the respiratory impedance over the whole lung volume. A new measurement protocol was introduced: subjects breathe slowly from residual volume to total lung capacity, while oscillations are applied simultaneously. The respiratory impedance is computed by means of the windowed cosine fitting method. The respiratory impedance becomes frequency- and volume-dependent Z(jω,V). The novel impedance-volume (ZV) diagram was introduced for diagnostic purposes. Least-squares parameter estimation was performed for the extended-RIC and the constant phase models. However, both models failed in presenting the volume-dependency of Z. We proposed two new models: the volume-dependent extended RIC and the volume-dependent Mead model for a better fit of the data. The volume-dependent Mead model yields the smallest estimation error.

This work introduces a novel technical extension of the Forced Oscillation Technique (FOT) by measuring the respiratory impedance over the whole lung volume at different frequencies. This method proposes a new measurement protocol in which patients breathe slowly from residual volume to total lung capacity, while oscillations are applied simultaneously on the spontaneous breathing. The respiratory impedance Z, computed by means of the windowed cosine fitting method, is frequency- and volume-dependent. The impedance-volume diagram was introduced as a diagnostic tool for abnormalities in lung mechanics. Data were obtained from healthy volunteers and volunteers with mild asthma. The volume-dependency of the respiratory impedance could be clearly observed in all data. In healthy subject, the respiratory resistance (the real part of Z) was maximum at the lowest lung volume and decreases at higher lung level, while the respiratory reactance (the imaginary part of Z) remain constant over a wide range of lung volume. Least-squares parameter estimation was performed on the data for the extended-RIC and the constant-phase models. As a result, both models failed in describing the volume-dependency of Z. Two new extended models were proposed for a better fit of the data: the volume-dependent extended-RIC and the volume-dependent Mead’s models. Overall, the volume-dependent Mead’s model yielded the smallest estimation error. Fig 1: The impedance-volume diagram