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Authors: Rui Santos 1 ; Joana Sousa 2 ; Borja Sañudo 3 ; Carlos J. Marques 4 and Hugo Gamboa 5

Affiliations: 1 FCT-UNL, Portugal ; 2 PLUX- Wireless Biosignals and S.A., Portugal ; 3 University of Seville, Spain ; 4 Faculty of Human Kinetics at the Technical University of Lisbon and Physical Therapy and Rehabilitation Department at the Schön Klinik Hamburg Eilbek, Portugal ; 5 FCT-UNL, PLUX- Wireless Biosignals and S.A., Portugal

Keyword(s): Biosignals, Signal-processing, Events, Detection and Identification, Signal-independent.

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 ; Health Engineering and Technology Applications ; 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

Abstract: This study presents a signal-independent algorithm, which detects significant events in a biosignal, without previous knowledge or specific pre-processing steps. From a morphological analysis, the algorithm computes the instants when the most significant standard deviation discontinuities occur. An iterative optimization step is then applied. This assures that a minimal error is achieved when modeling the signal segments (between the detected instants) with a polynomial regression. The detection scale can be modified by an optional input scale factor. An objective algorithm performance evaluation procedure was designed, and applied on two types of synthetic signals, for which the events instants were previously known. An overall mean error of 20.32 (+/-16.01) samples between the detected and the real events show the high accuracy of the proposed algorithm. The algorithm was also applied on accelerometry and electromyography raw signals collected in different experimental scenarios. T he fact that this approach does not require any previous knowledge and the good level of accuracy represents a relevant contribution in events detection and biosignal analysis. (More)


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Paper citation in several formats:
Santos, R.; Sousa, J.; Sañudo, B.; J. Marques, C. and Gamboa, H. (2012). BIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach. In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2012) - BIOSIGNALS; ISBN 978-989-8425-89-8; ISSN 2184-4305, SciTePress, pages 385-388. DOI: 10.5220/0003772403850388

author={Rui Santos. and Joana Sousa. and Borja Sañudo. and Carlos {J. Marques}. and Hugo Gamboa.},
title={BIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2012) - BIOSIGNALS},


JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2012) - BIOSIGNALS
TI - BIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach
SN - 978-989-8425-89-8
IS - 2184-4305
AU - Santos, R.
AU - Sousa, J.
AU - Sañudo, B.
AU - J. Marques, C.
AU - Gamboa, H.
PY - 2012
SP - 385
EP - 388
DO - 10.5220/0003772403850388
PB - SciTePress