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.
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