ROISES addresses the problem of inefficient ECG bio-signals management, which is mainly due to the diversity and heterogeneity of the various storage formats. ECG devices traditionally record ECGs in binary formats which are mainly proprietary, thus not inter- operable and comprehensible. Consequently, meaningful ECG data analysis is impeded, also considering that most data formats do not provide the means to store, the equally important, context and content meta-data of the signal. Biosignal context refers to information about patient demographics, diagnosis, recording equipment, researcher/investigator, etc., while annotation and interpretation information constitute part of the bio-signal content. Retrieving bio-signal metadata from other sources (if data are available and procedures are fea- sible) is often a prohibiting factor for important meta-analysis of the bio-signal
Mechanisms that would enable the combined access to bio-signals, their features and annotations along with the medical data that describe the context of the bio-signals, would provide a valuable tool, not only for knowledge discovery by the biomedical researcher community, but also for clinical use, in terms of medical evidence and clinical decision support, or even medical error filtering.
Within ROISES development four interesting points were distinguished:
- The definition of a method to integrate diverse biosignal datasources.
- The introduction of a standardized global ontology based on broadly accepted ECG domain standards and UMLS huge variety of source vocabularies and terminologies to virtually aggregate the diverse resources.
- The use of ontologies to support the decoupling of ECG information from the sources.
- The specification of a terminology enhancement method, based on UMLS lexicon and data-mining classification algorithms, to support the validity and extensibility of the integration scheme.