A Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data

TitleA Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data
Publication TypeJournal Article
Year of Publication2008
AuthorsChiang, J., Z. J. Wang, and M. J. McKeown
JournalSignal Processing, IEEE Transactions on
Volume56
Pagination4069 -4081
Date Publishedaug.
ISSN1053-587X
Keywordsautoregressive processes, dynamic contraction, electromyography, hidden Markov model, hidden Markov models, medical signal processing, motor behavior, multivariate autoregressive network framework, muscle activation, muscle connection pattern classification, pattern classification, surface EMG data analysis
Abstract

As the primary noninvasive means to assess muscle activation, the surface electromyogram (sEMG) is of central importance for the study of motor behavior in both clinical and biomedical applications. However, multivariate sEMG analysis is complicated by the fact that data recorded during dynamic contractions are inherently nonstationary. To model this nonstationarity and to determine the dynamic muscle activity patterns during reaching movements, we propose combining hidden Markov models (HMMs) and multivariate autoregressive (mAR) models into a joint HMM-mAR framework. We further propose constructing muscle networks statistically by performing a second level, group analysis on the subject-specific models. Network structural features are subsequently investigated as input features for the purpose of classification. The proposed approach was applied to real sEMG recordings collected from healthy and stroke subjects during reaching movements. When examining group muscle networks, we note that specific muscle connection patterns were selectively recruited during reaching movements and were differentially recruited after stroke compared to healthy subjects. As the analysis was performed on the raw data, the amplitude and the underlying #x201C;carrier data #x201D; of sEMG signals, we notice that the HMM-mAR model fits the amplitude data well, but not the raw or carrier data. The proposed sEMG analysis framework represents a fundamental departure from existing methods where only the amplitude is typically analyzed or the mAR coefficients are directly used for classification. As the method may provide additional insights into motor control, it appears a promising approach warranting further study.

URLhttp://dx.doi.org/10.1109/TSP.2008.925246
DOI10.1109/TSP.2008.925246

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