ABSTRACT
This paper presents a Lempel-Ziv complexity measure for analysis of surface electromyography signals. The Lempel-Ziv measure provides a metric for the number of distinct deterministic patterns and the rate of their creation in signals. We propose a ternary Lempel-Ziv measure, improving upon the binary Lempel-Ziv measure, and making it more suited for the analysis of biological signals. The Lempel-Ziv measure is evaluated with a muscle fatigue experiment in which participants perform static, cyclic, and random contractions. Results show this complexity measure shows a greater correlation to a steadily increasing muscle fatigue level compared to the conventional median frequency. This measure is computationally easy to compute and does not require power spectrum estimation and signal stationarity assumptions.
Subject(s)
Algorithms , Differential Threshold/physiology , Electromyography/methods , Exercise Test/methods , Muscle Contraction/physiology , Muscle Fatigue/physiology , Muscle, Skeletal/physiology , Adult , Diagnosis, Computer-Assisted/methods , Female , Humans , Male , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
Transcranial magnetic stimulation has become an established tool in experimental cognitive neuroscience and has more recently been applied clinically. The current spatial extent of neural activation is several millimeters but with greater specificity, transcranial magnetic stimulation can potentially deliver real time feedback to reinforce or extinguish behavior by exciting or inhibiting localized neural circuits. The specificity of transcranial magnetic stimulation is a function of the stimulation coil geometry. In this paper, a practical multilayer framework for the design of miniaturized stimulation coils is presented. This framework is based on a magnet wire fabricated from 2500 braided ultrafine wires. Effects of coil bending angle on stimulation specificity are examined using realistic finite element method simulations. A novel stimulation coil with one degree of freedom is also proposed that shows improved specificity over the conventional fixed coils. This type of coil could be potentially used as a feedback system for a bidirectional brain machine interface.
Subject(s)
Magnetics/instrumentation , Pattern Recognition, Automated/methods , Transcranial Magnetic Stimulation/instrumentation , Computer-Aided Design , Equipment Design , Equipment Failure Analysis , HumansABSTRACT
In this paper, we present a new method for multi-scale analysis of electromyography signals based on an interesting fractal process known as multiplicative cascade multi-fractal. Using simulated needle electromyography signals, we show this method provides a means for discrimination of normal and neuropathic electromyography signals. We also present experimental results that show the new parameters, computed using multiplicative cascade multi-fractal modeling, are more robust than the conventional signal parameter, number of turns, in the presence of additive noise. Results of multiplicative cascade multi-fractal modeling are consistent with other multi-scale approaches; advantages and differences are high lighted.
Subject(s)
Electromyography/methods , Humans , Models, Theoretical , Nervous System Diseases/diagnosisABSTRACT
This paper presents a novel multi-fractal detrended fluctuation analysis-based approach for fatigue estimation. This approach exploits the statistical self-similarity and long-range correlation of surface electromyography signals at different time scales in which the myoelectric manifestation of fatigue is more significant compared to the influence of varying force, muscle length (joint angle), and innervation zone. This approach provides a fatigue index which outperforms the conventional median frequency during cyclic and random contractions. This type of analysis is promising an efficient framework for analysis of surface electromyography signals with several potential applications.
Subject(s)
Algorithms , Electromyography/methods , Fractals , Muscle Contraction/physiology , Muscle Fatigue/physiology , Muscle, Skeletal/physiology , Pattern Recognition, Automated/methods , Adult , Female , Humans , Male , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
This paper presents a novel power spectrum-based method for fractal analysis of surface electromyography signals. This method, named the bi-phase power spectrum method, provides a bi-phase power-law which represents a multi-scale statistically self-affine signal. This form of statistical self-affinity provides an accurate approximation for stochastic signals originating from a strong non-linear combination of a number of similar distributions, such as surface electromyography signals which are formed by the summation of a number of single muscle fiber action potentials. This power-law is characterized by a set of spectral indicators, which are related to distributional and geometrical characteristics of the electromyography signal's interference pattern. These novel spectral indicators are capable of sensing the effects of motor units' recruitment and shape separately by exploiting the geometry of the interference pattern. The bi-phase power spectrum method is compared to geometrical techniques and the 1/f(alpha) approach for fractal analysis of electromyography signals. The extracted indicators using the bi-phase power spectrum method are evaluated in the context of force and joint angle and the results of a human study are presented. Results demonstrate that the bi-phase power spectrum method provides reliable information, consisting of components capable of sensing force and joint angle effects separately, which could be used as complementary information for confounded conventional measures.
Subject(s)
Algorithms , Diagnosis, Computer-Assisted/methods , Electromyography/methods , Fractals , Muscle Contraction/physiology , Muscle, Skeletal/physiology , Pattern Recognition, Automated/methods , Adult , Humans , MaleABSTRACT
In this paper we investigate the effect of force and joint angle on myoelectric signal parameters. In recent years, methods that have been previously used to analyze nonlinear chaotic dynamical systems have been applied to myoelectric signals. Nonlinear myoelectric signal parameters that have been used include the fractal dimension, estimated using the Katz method and Box-Counting methods, and the spectral slopes. Previous research has only examined effects of contractile force, whereas this research also includes joint angle effects. Results of this research suggest that the Katz and Box-Counting approaches used to estimate fractal dimension are not well suited for time functions, such as myoelectric signals. Results from the spectral slope parameters suggest that these parameters can track joint angle effects. A generalized model approach building upon the spectral slope method is proposed for future work.
