DocumentCode :
2923773
Title :
Shape analysis and clustering of Surface EMG Data
Author :
Boudaoud, Sofiane ; Ayachi, Fouaz ; Marque, Catherine
Author_Institution :
BMBI-CNRS UMR 6600 Lab., Univ. of Technol. of Compiegne (UTC), Compiègne, France
fYear :
2010
fDate :
Aug. 31 2010-Sept. 4 2010
Firstpage :
4703
Lastpage :
4706
Abstract :
Functional Data Analysis (FDA) is a recent field in data analysis and processing. It provides efficient methods and tools by considering the analyzed data as realizations of functions. In this discipline, raised shape analysis approaches. Among them, the Core Shape Modelling (CSM) furnished statistical tools for the evaluation of the shape dispersion among a set of curves. In this work, it is proposed to use this approach to study Surface EMG (SEMG) Data. These data represent electrical activity elicited during muscle contractions and measured on the surface of the skin. The generation of the SEMG signal is dependent on many morphological, physiological and neural parameters. In fact, the neural parameters tune the spatial and time recruitment of the Motor Units (MUs). In this study, the CSM algorithm is applied to detect MUs firing synchrony on SEMG data simulated using a realistic generation model. The generation parameters induce several variabilities and compensatory effects on SEMG data that could complicate and bias the data processing task. After phase realignment, a shape clustering is done on SEMG amplitude histograms using CSM formalism for different MU synchrony classes. The obtained results are promising and demonstrate the ability of shape analysis using the CSM approach to detect and classify MUs firing synchrony levels in SEMG data despite the present variabilities.
Keywords :
data analysis; electromyography; medical signal processing; neurophysiology; pattern clustering; CSM formalism; MU firing synchrony; SEMG amplitude histograms; core shape modelling; electrical activity; functional data analysis; motor units; muscle contractions; pattern clustering; shape analysis; surface EMG data; Electromyography; Fatigue; Force; Histograms; Muscles; Shape; Synchronization; Algorithms; Brain Mapping; Electroencephalography; Evoked Potentials, Motor; Humans; Imagination; Motor Cortex; Movement; Signal Processing, Computer-Assisted; Task Performance and Analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4123-5
Type :
conf
DOI :
10.1109/IEMBS.2010.5626378
Filename :
5626378
Link To Document :
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