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Authors: Neuza Nunes 1 ; Tiago Araújo 1 and Hugo Gamboa 2

Affiliations: 1 FCT-UNL, Portugal ; 2 CEFITEC / FCT - New University of Lisbon, Portugal

ISBN: 978-989-8425-35-5

Keyword(s): Biosignals, waves, Unsupervised learning, Clustering, Data mining, Signal-processing.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Detection and Identification ; Devices ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Wearable Sensors and Systems

Abstract: In this paper we introduce an unsupervised learning algorithm which distinguishes two different modes in a cyclic signal. We also present the concept of “mean wave” which averages all signal waves aligned in a notable point (nth zero derivative). With that information the signal’s morphology is captured. The clustering mechanism is based on the information collected with the mean wave approach using a k-means algorithm. The algorithm produced is signal-independent, and therefore can be applied to any type of signal providing it is a cyclic signal that has no major changes in the fundamental frequency. To test the effectiveness of the proposed method, we acquired several biosignals (accelerometry, electromyography and blood volume pressure signals) in the context tasks performed by the subjects with two distinct modes in each. The algorithm successfully separates the two modes with 99.2% of efficiency. The fact that this approach doesn’t require any prior information and the preliminar y good classification performance makes this algorithm a powerful tool for biosignals analysis and classification. (More)

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Paper citation in several formats:
Nunes, N.; Araújo, T. and Gamboa, H. (2011). TWO-MODES CYCLIC BIOSIGNAL CLUSTERING BASED ON TIME SERIES ANALYSIS .In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 257-264. DOI: 10.5220/0003165002570264

@conference{biosignals11,
author={Neuza Nunes. and Tiago Araújo. and Hugo Gamboa.},
title={TWO-MODES CYCLIC BIOSIGNAL CLUSTERING BASED ON TIME SERIES ANALYSIS },
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={257-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003165002570264},
isbn={978-989-8425-35-5},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - TWO-MODES CYCLIC BIOSIGNAL CLUSTERING BASED ON TIME SERIES ANALYSIS
SN - 978-989-8425-35-5
AU - Nunes, N.
AU - Araújo, T.
AU - Gamboa, H.
PY - 2011
SP - 257
EP - 264
DO - 10.5220/0003165002570264

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