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Biostatistics 4:143-164 (2003)
© 2003 Oxford University Press

A Bayesian change-point analysis of electromyographic data: detecting muscle activation patterns and associated applications

Timothy D. Johnson{dagger}, Robert M. Elashoff and Susan J. Harkema

Department of Biostatistics, School of Public Health, Univeristy of Michigan, Ann Arbor, MI 48109, USA tdjtdj{at}umich.edu
Department of Biomathematics, UCLA School of Medicine, Los Angeles, CA 90095, USA
Department of Neurology and The Brain Research Institute, UCLA School of Medicine, Los Angeles, CA 90095, USA

{dagger}To whom correspondence should be addressed

Many facets of neuromuscular activation patterns and control can be assessed via electromyography and are important for understanding the control of locomotion. After spinal cord injury, muscle activation patterns can affect locomotor recovery. We present a novel application of reversible jump Markov chain Monte Carlo simulation to estimate activation patterns from electromyographic data. We assume the data to be a zero-mean, heteroscedastic process. The variance is explicity modeled using a step function. The number and location of points of discontinuity, or change-points, in the step function, the inter-change-point variances, and the overall mean are jointly modeled along with the mean and variance from baseline data. The number of change-points is considered a nuisance parameter and is integrated out of the posterior distribution. Whereas current methods of detecting activation patterns are deterministic or provide only point estimates, ours provides distributional estimates of muscle activation. These estimates, in turn, are used to estimate physiologically relevant quantities such as muscle coactivity, total integrated energy, and average burst duration and to draw valid statistical inferences about these quantities.

Keywords: B-Splines; Change-point analysis; Electromyography; EMG; Non-constant variance; Reversible jump Markov chain Monte Carlo; Step-function


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