Speaker
Description
Physiological measurements are permanently acquired in daily clinical practice to aid medical staff in decision making.Electrocardiography (ECG) is one of the most commonly used measurements and represents the physiological activity of the heart. It is widely used in emergency care as well as prevention and in clinical studies. Therefore, it offers large potential for data-driven research, e.g. machine learning for disease prediction in acure care or risk assessment for chronic diseases.
However, the collected data is often stored in proprietary devices or printed on paper which limits its potential for advanced analysis methods. Moreover, this propreritary nature leads to negative effect w.r.t. open science: Algorithms are oftentimes written once for a specific research project and are dependent on the data formats used, making them non-reusable.
To overcome these limitations, we aim to develop an interoperable architecture in the framework of the BMBF-funded ACRIBiS project (Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis). Our goal is to store and exchange ECG data in a findable, accessible, interoperable, and reusable (FAIR) manner. Moreover, we aim for standardized access, selection, filtering, rights management, and user management, enabling scalable use in clinical studies.
As an initial implementation, the objective is to process signal analysis on ECGs stored in a FAIR manner via the AcuWave Software Suite. We evaluated multiple open source solutions for storing our DICOM ECGs. We initially employed Orthanc, which provides access to the ECGs via its Representational State Transfer - Application Programming Interface. This was utilized to construct a pipeline that can load the ECGs into AcuWave, however, we encountered performance and administrative issues. Consequently, a transition to XNAT is being considered as a potential solution to address these challenges. Within AcuWave, the ECGs are processed in a standardised and modular manner, following a pipeline approach. This architecture will be demonstrated, along with the potential benefits of its automation for research.
