Nonlinear methods of analysis for biological signals

我们将研究为分析非线性和混沌系统而开发的技术，并将它们应用于生物信号。特别是，我们感兴趣的是开发一种强大的系统，能够以非侵入性的方式监测肌肉疲劳。目前的“黄金标准”检查肌肉电信号(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，频谱会向较低的频率移动，导致中值频率降低；然而，当收缩的力和/或关节角度改变时，中值频率也会发生变化，这给这种方法带来了混乱，限制了其实用价值。我们正在开发一种新的方法，该方法基于肌电信号频谱的广义随机尺度分形模型。初步结果已经表明，这种方法可以区分收缩力量、关节角度和肌肉疲劳的不同影响。这将使构建一个能够监测肌肉疲劳的系统成为可能，不仅可以监测静态收缩，还可以监测动态收缩。研究可靠的非侵入性肌肉疲劳监测方法在人体工效学、损伤预防、运动医学和人体运动能力等领域具有重要的应用意义。这些分析方法在肌肉活动起始检测中也有实用价值。肌肉活动的起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，用于起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，要么噪音被错误地贴上了肌电数据的标签，要么就是遗漏了低水平的收缩。这项研究将利用这样一个事实，即与背景噪声相比，肌电信号表现出与其分形几何和持续性相关的不同参数。从噪声中区分肌电信号的能力将提高自动起搏检测的准确性和稳健性。这将为研究人员和临床医生节省时间，也有助于其他实时肌电信号分析系统的开发。

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.