Nonlinear methods of analysis for biological signals

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We will be researching techniques developed for the analysis of nonlinear and chaotic systems and applying them to biological signals. In particular, we are interested in developing a robust system that is capable of monitoring muscular fatigue in a noninvasive manner. The current "gold standard" examines the spectrum of the myoelectric signals (electrical signals associated with muscle contractions), tracking the median frequency. As a muscle fatigues, the spectrum will shift towards lower frequencies, resulting in a decreased median frequency; however, the median frequency will also change when the force of the contraction and/or the joint angle is altered, which confounds this method and limits its practical utility. We are developing a novel method based on a Generalized Random Scaling Fractal model of the myoelectric signal spectrum. Initial results already indicate that this methodology can separate the different effects of contractile force, joint angle, and muscular fatigue. This will enable to construction of a system that is capable of monitoring muscular fatigue, not only for static contractions, but also for dynamic contractions. Research into reliable noninvasive methods for monitoring muscle fatigue has implications in application areas such as ergonomics, injury prevention, sports medicine, and human performance. These analysis methods also have utility in muscle activity onset detection. The onset time of muscle activity is a fundamental characteristic used in biomechanics and motor control research; however, analysis of the myoelectric signal for onset detection is often conducted manually, or if an automated system is used, manual correction is often still required. The problem is that either noise is falsely labeled as myoelectric data, or low levels of contractions are missed. This research will be leveraging the fact that myoelectric signals exhibits different parameters associated with its fractal geometry and persistence, as compared to its background noise. The ability to discern myoelectric signals from the noise will improve the accuracy and robustness of automatic onset detection. This will save time for researchers and clinicians, but also assist in the development of other real-time myoelectric signal analysis systems.