PEAS: parametric EIT analysis software, a software to perform analyses on electrical impedance tomography data

Electrical impedance tomography (EIT) is a powerful imaging technique for assessing regional ventilation, but its analysis remains challenging due to the diversity of input formats, acquisition protocols, and research objectives. This work aims to simplify and standardize EIT data analysis through the development of a modular, user-friendly software platform.

Approach: We developed the Parametric EIT Analysis Software (PEAS), a modular platform for EIT data analysis based on configurable, template-driven workflows. The software supports both raw voltage data with integrated image reconstruction and pre-reconstructed images, provides temporal detectors for breathing cycles and maneuvers, and reusable analysis components. These functionalities are accessed through a graphical user interface that enables interactive workflow configuration and execution.

Main results: The implemented framework supports multiple vendor-specific data formats, including both raw voltage recordings and reconstructed image data. It provides automated detection of breathing cycles and respiratory maneuvers, as well as over 40 generic building blocks that can be combined into customized analysis pipelines. Typical workflows execute within seconds on standard hardware, enabling interactive use. A questionnaire-based user study indicated that the software is easy to learn and operate.

Significance: By providing a standardized, extensible, and user-friendly environment for EIT data analysis, PEAS lowers the technical barrier to applying EIT in both research and clinical practice. This platform supports reproducibility, interoperability, and wider adoption of EIT for physiological monitoring and diagnostic applications. By offering a standardized yet extensible environment for EIT data analysis, PEAS reduces technical barriers in both research and clinical contexts. The platform promotes reproducibility, interoperability, and broader adoption of EIT for physiological monitoring and diagnostic applications.