Robust electrical impedance tomography for biological application: A mini review

Electrical impedance tomography (EIT) has been used by researchers across several areas because of its low-cost and no-radiation properties. Researchers use complex conductivity in bioimpedance experiments to evaluate changes in various indicators within the image target. The diverse volumes and edges of biological tissues and the large impedance range impose dedicated demands on hardware design. The EIT hardware with a high signal-to-noise ratio (SNR), fast scanning and suitable for the impedance range of the image target is a fundamental foundation that EIT research needs to be equipped with. Understanding the characteristics of this technique and state-of-the-art design will accelerate the development of the robust system and provide a guidance for the superior performance of next-generation EIT. This review explores the hardware strategies for EIT proposed in the literature.

Differential Pressure Spirometry for Mechanical Ventilation Using Dichotomic Search

In invasive mechanical ventilation (IMV), it is critical that the flow value is estimated correctly, as it is used as a trigger variable for ventilatory assistance. Furthermore, the numerical integration of the flow allows the calculation of the total volume per breath (tidal volume), which clinicians use to identify trauma or lung capacity in the patient. The current COVID-19 pandemic has demonstrated the need to develop safe and efficient techniques for measuring this spirometry variable because many mechanical ventilators delivered to hospitals were unable to measure it directly. A good device to estimate flow is a D-lite sensor, which works by the Venturi effect, is cheap, reusable, and proximal to the patient. However, the regressions applied to the flow estimation model are limited for use in real conditions. This article presents a flow estimation method that uses a D-Lite device, a fraction of inspired oxygen (FiO2) cell, and two pressure sensors as critical items. Our novel method adapts the dichotomous search algorithm instead of conventional regression algorithms to estimate flow using a D-lite sensor; this change in the standard procedure allowed us a fast calibration process, a good low-flow estimation, and low computational time for flow estimation. The method was validated experimentally to compute the tidal volume according to the measurement requirement error range of +/-10%. The consideration of FiO2 percentage in the gas mixture and the good low-flow estimation make this novel method useful for real ventilation conditions. The flow calculations have been performed at different ambient conditions and compared with gas analyzers show an average relative error of up to 4.86%. Finally, we present an analysis of the error flow estimation considering the variation in each variable. Technical recommendations for applying this novel method to achieve IMV safely are presented, based on the capabilities of the embedded system used by developers.

Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation

BACKGROUND

Optimisation of mechanical ventilation (MV) and weaning requires insight into underlying patient breathing effort. Current identifiable models effectively describe lung mechanics, such as elastance (E) and resistance (R) at the bedside in sedated patients, but are less effective when spontaneous breathing is present. This research derives and regularises a single compartment model to identify patient-specific inspiratory effort.

METHODS

Constrained second-order b-spline basis functions (knot width 0.05 s) are used to describe negative inspiratory drive (Pp, cmH₂O) as a function of time. Breath-breath Pp are identified with single E and R values over inspiration and expiration from n = 20 breaths for N = 22 patients on NAVA ventilation. Pp is compared to measured electrical activity of the diaphragm (Eadi) and published results.

RESULTS

Average per-patient root-mean-squared model fit error was (median [interquartile range, IQR]) 0.9 [0.6-1.3] cmH₂O, and average per-patient median Pp was -3.9 [-4.5- -3.0] cmH₂O, with range -7.9 - -1.9 cmH₂O. Per-patient E and R were 16.4 [13.6-21.8] cmH₂O/L and 9.2 [6.4-13.1] cmH₂O.s/L, respectively. Most patients showed an inspiratory volume threshold beyond which Pp started to return to baseline, and Pp at peak Eadi (end-inspiration) was often strongly correlated with peak Eadi (R²=0.25-0.86). Similarly, average transpulmonary pressure was consistent breath-breath in most patients, despite differences in peak Eadi and thus peak airway pressure.

CONCLUSIONS

The model-based inspiratory effort aligns with electrical muscle activity and published studies showing neuro-muscular decoupling as a function of pressure and/or volume. Consistency in coupling/dynamics were patient-specific. Quantification of patient and ventilator work of breathing contributions may aid optimisation of MV modes and weaning.