Nonlinear methods of analysis for biological signals

我们将研究用于分析非线性和混沌系统的技术，并将其应用于生物信号。特别是，我们有兴趣开发一个强大的系统，能够以非侵入性的方式监测肌肉疲劳。目前的“黄金标准”检查肌电信号（与肌肉收缩相关的电信号）的频谱，跟踪中值频率。当肌肉疲劳时，频谱将向较低频率移动，导致中值频率降低;然而，当收缩力和/或关节角度改变时，中值频率也将改变，这使该方法混淆并限制其实际效用。我们正在开发一种新的方法的基础上的广义随机标度分形模型的肌电信号频谱。初步结果已经表明，这种方法可以区分收缩力、关节角度和肌肉疲劳的不同影响。这将使得能够构建能够不仅针对静态收缩而且针对动态收缩监测肌肉疲劳的系统。对监测肌肉疲劳的可靠的非侵入性方法的研究在诸如人体工程学、损伤预防、运动医学和人类表现等应用领域具有意义。这些分析方法在肌肉活动开始检测中也具有实用性。肌肉活动的开始时间是生物力学和运动控制研究中使用的基本特征;然而，用于开始检测的肌电信号的分析通常是手动进行的，或者如果使用自动化系统，则通常仍然需要手动校正。问题是，要么噪声被错误地标记为肌电数据，要么低水平的收缩被错过。这项研究将利用这样一个事实，即肌电信号与其背景噪声相比，表现出与其分形几何和持久性相关的不同参数。从噪声中辨别肌电信号的能力将提高自动发作检测的准确性和鲁棒性。这将为研究人员和临床医生节省时间，也有助于开发其他实时肌电信号分析系统。

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.