论文标题
一种迭代的翘曲和聚类算法，可从非平稳振荡信号估算多个波形函数
An iterative warping and clustering algorithm to estimate multiple wave-shape functions from a nonstationary oscillatory signal
论文作者
论文摘要
非无曲线振荡信号无处不在。实际上，以1个周期波形功能（WSF）建模的非肌体振荡模式可能因周期而异。当有不同的WSF，$ s_1，\ ldots，s_k $时，因此WSF突然跳到另一个，不同的WSF和跳跃编码有用的信息。我们提出了一种迭代翘曲和聚类算法，以估算来自非平稳振荡信号的$ s_1，\ ldots，s_k $，具有随时间变化的振幅和频率，因此WSF的变化点。该算法是时间频率分析，奇异值分解熵和矢量光谱聚类的新型组合。我们通过模拟和真实信号（包括语音信号，动脉血压，心电图和加速度计信号）来证明所提出的算法的效率。此外，我们在信号的幅度和频率缓慢时变化的假设下提供了算法的数学合理性，并且存在有限的变化点，可以模拟从一个波形函数到另一个波形函数的突然变化。
Nonsinusoidal oscillatory signals are everywhere. In practice, the nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function (WSF), might vary from cycle to cycle. When there are finite different WSFs, $s_1,\ldots,s_K$, so that the WSF jumps from one to another suddenly, the different WSFs and jumps encode useful information. We present an iterative warping and clustering algorithm to estimate $s_1,\ldots,s_K$ from a nonstationary oscillatory signal with time-varying amplitude and frequency, and hence the change points of the WSFs. The algorithm is a novel combination of time-frequency analysis, singular value decomposition entropy and vector spectral clustering. We demonstrate the efficiency of the proposed algorithm with simulated and real signals, including the voice signal, arterial blood pressure, electrocardiogram and accelerometer signal. Moreover, we provide a mathematical justification of the algorithm under the assumption that the amplitude and frequency of the signal are slowly time-varying and there are finite change points that model sudden changes from one wave-shape function to another one.
