Electrocardiogram (ECG) data have been traditionally generated by multiple software applications on various platforms. Furthermore local data storage and distribution uses different formats and structures. These data modelling and distribution tasks should consist of flexible and inexpensive tools to enhance pattern recognition and visualisation capabilities of humans and machines. There is an increased need to promote the development of standards in order to support a seamless exchange and migration of ECG data as well as the native integration into Electronic Patient Records (EPR) and medical guidelines. Such models should be platform-independent, flexible and open to the scientific community. In the case of ECG data interpretation, an important pre-requisite is a comprehensive data description independent of the number of channels, instrumentation platform and type of experiments. Additionally, an ECG record should include annotations relating to the acquisition protocols, patient information and analysis results.

The Food and Drug Administration (FDA), Center for Drug Evaluation and Research, has proposed recommendations for the exchange of time-series data. The projected standard consists of a hierarchical data structure for the representation of signals, including ECG data, which ideally would be encoded in an XML file. Recent advances include I-Med, which is a XML -based format for clinical data bundled with a domain-independent interface for exchanging several types of medical information. Its major goal is to provide a unique platform for clinical transactions. I-Med messages can include ECG records, which may be described by basic features, such as QRS duration (i.e. time interval necessary for ventricular depolarization) and text-based interpretations.

This paper discusses the minimum set of information needed for a meaningful representation and storage of electrocardiogram signals. It has been synthesized from existing recommendations and compiled into an XML schema (ecgML). The accompanying application comfortably supports medical tasks such as pattern recognition and identification of relevant wave markers. More recently, eXtensible Stylesheet Language (XSL) transformations are developed to convert "raw" ecgML files into data mining formats such as MatLab (for further analysis), Scalable Vector Graphics (SVG) (for graphical visualisation) and audio format. Thus, ecgML is a useful tool to facilitate the representation, exchange and interpretation of ECG information.