Determination of muscle fatigue using dynamically embedded signals

There is concern associated with the duration that a micro-surgeon operates. Muscle fatigue can present itself over time and adversely affect the surgeon's ability to perform appropriately during lengthy procedures. This paper explores a new method of analyzing muscle fatigue within the muscles predominantly used during micro-surgery. The captured Electro-MyoGraphic (EMG) data retrieved from these muscles are analyzed for any defining patterns relating to muscle fatigue. The analysis consists of dynamically embedding the EMG signals from a single muscle channel into an embedded matrix. The muscle fatigue is determined by defining its entropy characterized by the singular values of the Dynamical Embedded (DE) matrix. The paper compares this new method with the traditional method of using mean frequency shifts in EMG signal's power spectral density. Linear regressions are fitted to the results from both methods, and the coefficient of variation of both their slope and point of intercept are determined. It is shown that the complexity method is more robust in that the coefficient of variation for the DE method has lower variability than the conventional method of mean frequency analysis.