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Notes:
Volume 6, Issue 4 (Suppl)
J Spine, an open access journal
ISSN: 2165-7939
Page 48
July 24-26, 2017 Rome, Italy
&
Spine and Spinal Disorders
2
nd
International Conference on
Neurology and Neuromuscular Diseases
6
th
International Conference on
CO-ORGANIZED EVENT
Reduction of crosstalk in surface electromyogram by optimal spatio-temporal filtering
Luca Mesin
1
and
Imran Khan Niazi
2
1
Polytechnic University of Turin, Italy
2
Aalborg University, New Zealand
C
rosstalk can pose limitations in the applications of surface electromyogram (EMG). Its reduction can help in the identification
of the activity of specific muscles. The selectivity of different spatial filters was tested in the literature both in simulations and
experiments, but their performances are affected by many factors (e.g., anatomy and dimension/location of the electrodes). Moreover,
they reduce crosstalk by decreasing the detection volume, recording data that represent only the activity of a small portion of the
muscle of interest. In this study we propose an adaptive approach, which filters both in time and among different channels, providing
a signal that maximally preserves the energy of the EMG of interest and discards that of nearby muscles (increasing the signal to
crosstalk ratio, SCR). Tests with simulations and experimental data show an average increase of the SCR of about 2 dB with respect
to the SD or DD data processed by the filters. The method is applied to few signals, proving its potential in applicative studies (e.g.,
clinics, gate analysis, and prosthesis control) where a limited number of non-selective channels are used.
Biography
Luca Mesin has done his Master’s degree in Electronics Engineering in 1999 and PhD in Applied Mathematics in 2003. He is an Associate Professor in Biomedical
Engineering at Polytechnic University of Turin, Italy. He is the Head of the research group on Mathematical Biology and Physiology. His research activities are
devoted to the processing of signals or images extracted from biological and physiological systems and to the development of mathematical models for the
interpretation of the recorded data. Applications are mainly focused on the investigation of biological systems or on the development of new biomedical tools.
Recent works concern the simulation of spiral waves using a model of electromechanical coupling in the heart, the investigation of the central venous pressure, the
processing of multiple data reflecting the responses of the autonomous nervous system and the simulation and processing of bioelectric signals.
luca.mesin@polito.itLuca Mesin et al., J Spine 2017, 6:4(Suppl)
DOI: 10.4172/2165-7939-C1-005