Authors:
Susana Brás
1
;
Nuno Ferreira
2
and
João Paulo Silva Cunha
1
Affiliations:
1
Aveiro University and Universidade de Aveiro, Portugal
;
2
Biodevices S.A., Portugal
Keyword(s):
Electrocardiogram, Artefact detection, Threshold analysis.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cardiovascular Signals
;
Computer Vision, Visualization and Computer Graphics
;
Detection and Identification
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
Newly devices allow the analysis and collection of very long-term electrocardiogram (ECG). However, associated with this devices and long-term signal, are artefacts that conduce to misleading interpretations and diagnosis. So, new developments over automatic ECG classification are needed for a reliable interpretation. The feasibility of the cardiac systems is one of the main concerns, once they are currently used as diagnosis or help systems. In this project, an artefact detection algorithm is developed, dividing the time-series in intervals of signal and artefact. The algorithm is based on the assumption that, if the analysed frame is signal, there is not an abrupt alteration over consecutive short windows. So, the time-series is divided in consecutive nonoverlapped short windows. Over these windows, it is calculated the time-series standard deviation, the maximum and minimum slope. A threshold-based rule is applied, and the algorithm reveals that, in mean, it is verified a 99.29% o
f correctly classified signal and only 0.71% of signal erroneously classified. Over the results obtained, the algorithm seems to present good results, however it is needed its validation in a wider and representative sample with segments marked as artefact by multiple specialists.
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