Authors:
Ricardo Gomes
1
;
Neuza Nunes
2
;
Joana Sousa
2
and
Hugo Gamboa
3
Affiliations:
1
FCT-UNL, Portugal
;
2
PLUX Wireless Biosignals S.A., Portugal
;
3
FCT-UNL and PLUX Wireless Biosignals S.A., Portugal
Keyword(s):
Biosignal, signal processing, Long term monitoring, Data structure.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Cybernetics and User Interface Technologies
;
Devices
;
Human-Computer Interaction
;
Information and Systems Security
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Monitoring and Telemetry
;
Physiological Computing Systems
Abstract:
Long term acquisitions of biosignals are an important source of information about the patients’ state and its evolution, but involves managing very large datasets, which make signal visualization and processing a complex task. To overcome these problems, we introduce a new data structure to manage long term biosignals. A fast and non-specific multilevel biosignal visualization tool based on the concept of subsampling is presented, with focus on the representative signal parameters (mean, maximum, minimum and standard deviation error). The visualization tool enables an overview of the entire signal and a more detailed visualization in specific parts which we want to highlight. The ”Split and Merge” concept is exposed for long term biosignals processing. A processing tool (ECG peak detection) was adapted for long term biosignals. Several long term biosignals were used to test the developed algorithms. The visualization tool has proven to be faster than the standard methods and the deve
loped processing algorithm detected the peaks of long term ECG signals fast and efficiently. The non-specific character of the new data structure and visualization tool, and the speed improvement in signal processing introduced by these algorithms makes them useful tools for long term biosignals visualization and processing.
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