New Visualization Model for Large Scale Biosignals Analysis

Catarina Cavaco, Ricardo Gomes, Hugo Gamboa, Ricardo Matias

Abstract

The development of new resources in the medical field, such as wearable sensors, allowed the improvement of biosignals monitoring. Acquired data is then an important source of information to clinicians and researchers. Thus, extracting useful information from data is a task of the greatest importance that involves a variety of concepts and methods, from which stands out data visualization. However, these methods present several limitations mainly when dealing with big data. In this paper we present an innovative web-based application for biosignals visualization and exploration in a fast and user friendly way overcoming the detected limitations. Three case studies are presented and a usability study supports the reliability of the implemented work.

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Paper Citation


in Harvard Style

Cavaco C., Gomes R., Gamboa H. and Matias R. (2015). New Visualization Model for Large Scale Biosignals Analysis . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 190-197. DOI: 10.5220/0005207201900197


in Bibtex Style

@conference{biosignals15,
author={Catarina Cavaco and Ricardo Gomes and Hugo Gamboa and Ricardo Matias},
title={New Visualization Model for Large Scale Biosignals Analysis},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={190-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005207201900197},
isbn={978-989-758-069-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - New Visualization Model for Large Scale Biosignals Analysis
SN - 978-989-758-069-7
AU - Cavaco C.
AU - Gomes R.
AU - Gamboa H.
AU - Matias R.
PY - 2015
SP - 190
EP - 197
DO - 10.5220/0005207201900197