SignalML: metaformat for description of biomedical time series

Durka, P.J.; Ircha, D.

doi:10.1016/j.cmpb.2004.05.007

Cited by 13 publications

(14 citation statements)

References 6 publications

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“…9). This multiplatform system written in Java relies on the SignalML metadescription of data formats (Durka and Ircha 2004) to compile on the fly appropriate codecs for input data. Matlab Builder for Java (MBfJ, http:// mathworks.com/products/javabuilder) was used to build a platform independent runtime of the modules implementing the artifacts detection.…”

Section: Information Sharing Statementmentioning

confidence: 99%

On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings

et al. 2009

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We present an open, parametric system for automatic detection of EEG artifacts in polysomnographic recordings. It relies on independent parameters reflecting the relative presence of each of the eight types of artifacts in a given epoch. An artifact is marked if any of these parameters exceeds a threshold. These thresholds, set for each parameter separately, can be adjusted via "learning by example" procedure (multidimensional minimization with computationally intensive cost function), which can be used to automatically tune the parameters to new types of datasets, environments or requirements. Performance of the system, evaluated on 103 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the inter-expert agreement. To make this statement well defined, we review the methodology of evaluation for this kind of detection systems. Complete source code is available from http://eeg.pl; a user-friendly version with Java interface is available from http://signalml.org.

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Section: Information Sharing Statementmentioning

confidence: 99%

On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings

et al. 2009

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“…In addition to the FDA XML Data Format, similar efforts have been also made to introduce solutions to the problem of inherent incompatibility of different formats used for digital storage of biological data (in particular EEG) and their annotations; such formats include SignalML [10]. However, SignalML fails to provide much background information related to the description of already-known variables with regard to a specified EEG recording.…”

Section: B Accompanying Toolsmentioning

confidence: 96%

“…Conversion of data stored in EDF or EDF+ formats to other formats is required before these data can be used directly in certain applications. However, conversion between different data formats in certain cases may lead to a loss of information or accuracy [10].…”

mentioning

confidence: 99%

An XML format for electroencephalogram data presentation (eegML)

Zhang

Zhou

et al. 2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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With the development of the electronic health record, sharing biomedical and healthcare data among heterogeneous systems has great potential to benefit both clinical healthcare and scientific research. Standardizing the data presentation is the best solution to exchange them among heterogeneous systems. However, electroencephalogram (EEG) data has met problems since existing diverse data formats are unsuitable for this purpose. To address this problem we introduce an XML-based data presentation method named eegML for EEG in this paper. Based on advantages of XML technologies, eegML is a generic system-and applicationindependent and flexible open solution to the effective presentation of EEG data. Besides, eegML could also be used to aid knowledge management and discovery in biomedical and healthcare informatics. This format, if widely adopted, could better promote EEG data management and mining.

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“…Once written, this information can be used by any software, which, owing to this metadescription, may read the original data files, thus eliminating the need for conversions and duplication of data. [8] Another interesting effort is ecgML which presents a system-application-and format-independent solution for representation and exchange of ECG data, based on advantages of XML technologies. Moreover, a distinct separation of content and presentation (among other components such as links and semantic) exhibits a remarkable advantage over existing systems where information is merged and intertwined with its representation format [9].…”

Section: B Ontologies and Related Workmentioning

confidence: 99%

An ontology-based approach facilitating unified querying of biosignals and patient records

Kokkinaki

Chouvarda

Maglaveras

2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Electrocardiography serves as the gold standard for the noninvasive diagnosis of heart disease. Comparison and review between current and previous ECG recordings and clinical data may considerably leverage the physician's diagnostic accuracy. However, clinical data and ECGs are located in heterogeneous and disparate computer systems. On top of this, the plethora of different and mainly proprietary formats for ECG recording and storing, impedes the efforts towards aggregation of medical information. In this paper we propose an approach towards the seamless integration of and access to disparate and heterogeneous sources of medical information, through a semantically enhanced platform called SCP-ECG Integrated Access (SEIA). SEIA provides the means to contextualize SCP-ECG biosignals, by semantically structuring the clinical information they contain. The semantic annotation procedure is enhanced with further processing of the biosignal, including waveform recognition and feature extraction, for automatic or semiautomatic insertion of annotations where missing. Diagnosis and evaluation of a patient's clinical condition is facilitated through integrated temporal comparison of the medical information derived from successive ECGs, in terms of their features and annotations, along with clinical data and measurements originating from the Electronic Medical Record (EMR).

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SignalML: metaformat for description of biomedical time series

Cited by 13 publications

References 6 publications

On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings

On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings

An XML format for electroencephalogram data presentation (eegML)

An ontology-based approach facilitating unified querying of biosignals and patient records

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