Invasive mechanical ventilation is the cornerstone of respiratory failure treatment. Setting ventilator parameters is a balancing act between providing adequate oxygenation and ventilation on the one hand, and minimizing lung damage and adverse cardiovascular effects on the other hand. This thesis aims to find the differences between two methods for the titration of positive end-expiratory pressure: electrical impedance tomography and esophageal pressure monitoring. Moreover, methods are proposed to suppress cardiac activity from electrical impedance tomography and esophageal pressure recordings.

Data analysis for electrical impedance tomography (EIT) research requires manual selection of sequences with a normal breathing pattern. This procedure is lengthy and the lack of a standardised approach results in different practices among EIT studies, limiting the potential and comparability of the research.
This article presents a new approach to solving this problem, using automatic detection of EIT sequences with normal breathing pattern. An algorithm was developed to differentiate between normal and disturbed breathing patterns. To facilitate data analysis, it was implemented in an application that allows for EIT parameter calculation of selected sequences. EIT recordings of three patients recruited in an observational study were used to develop the algorithm. A reference standard was defined as the majority vote of five biomechanical engineering students performing manual classification. Frequency and time domain properties were compared between the signals that these graders classified as reliable and unreliable, and were used to define classification rules for the algorithm. Matlab was used to create the classification algorithm and implement it in an application. The developed algorithm was validated with a new data set containing EIT recordings of an additional three patients, classified by the same volunteers. Qualitative analysis was performed to investigate the causes of conflict between manual and automated data selection.
The resulting algorithm achieved a sensitivity of 92.8% (95% CI, 92.6%-92.9%) and a specificity of 85.5% (95% CI, 85.1%-85.8%) on the EIT files used for development. On the validation set the algorithm accomplished a sensitivity of 86.5% (95% CI, 86.3%-86.7%), and specificity of 79.7% (95% CI, 79.4%-80.0%).
Most differences between manual and automatic data selection were found to be around the edges of the selected sequences. Other discrepancies can be explained by difference in data selection behaviour for varying recording qualities during manual selection. The presented algorithm proves its potential for quick and reliable data classification of EIT recordings. It not only provides a new standard for data selection in EIT research, but also reduces the time investment of researchers. The freely available data analysis application enables easy implementation of the algorithm. The presented study therefore provides a first step towards a uniform approach in EIT research, improving comparability of studies and increasing the scientific value of their findings.