HeiDATAProVIT-Heidelberg data archiving, tag assembling, processing and visualization tool

The demands that have to be met by software tools for biomedical data evaluation strongly differ depending on the background of their application. In clinical routine emphasis is placed on ease of handling and application of standardized procedures, whereas in biomedical research the main focus lies on flexibility and extensibility. These contradictory requirements are reflected by the design principles of existing software solutions: programs for routine application are barely extensible or modifiable by the user and the complexity of highly flexible data processing tools for research purposes hampers the application of new methods to larger data volumes. This gap poses technical difficulties to the transfer of methods from research into clinical routine. The software we present in this paper bridges this discrepancy by incorporating two different levels of application. The lower level offers options to integrate custom written MATLAB((R)) processing routines and to add new evaluation schemes to a pool of existing procedures. The higher level allows for performing standard evaluations by accessing and applying these previously defined procedures. Four basic concepts were introduced to ensure that the program is both maximally flexible on a lower level and readily applicable on a higher level: the tag concept, the concept of modularized visualization, the dummy file concept, and the batchjob concept. These concepts are the key to flexibly assemble and apply the three universal stages of data evaluation: (1) archiving of acquired data, (2) processing the data using signal processing algorithms and (3) visualizing the results in appropriate graphical formats. The present paper illustrates the four concepts within the two levels of the software architecture. The basic functionality and usefulness of the program are demonstrated using an evaluation of gait analysis data as sample application. In summary, this software tool closely integrates a database for biomedical datasets and an extensible pool of evaluation and visualization procedures realized using MATLAB((R)). It is well suited both for data processing in clinical routine and for evaluation of measurement data in any medical research project.