Jacobs CHRISTOPHER R., Yellowley CLARE E., Nelson DREW V., Donahue HENRY J.
Musculoskeletal Research Laboratory, Department of Orthopaedics and Rehabilitation, Pennsylvania State University, USA.
Comput Methods Biomech Biomed Engin. 2000;3(1):31-40. doi: 10.1080/10255840008915252.
A wide range of biological investigations lead to time-history data. The characterization of such data can be difficult particularly in the presence of signal noise or superimposed signals. Several methods are described which can be brought to bear including FFT, thresholding, peak counting, and range counting. However, each of these approaches has significant disadvantages. In this paper we describe a novel method, known as rainflow cycle counting, for characterizing time varying biological time-history data in terms of spiking or oscillation amplitude and frequency. Rainflow counting is a straightforward algorithm for identifying complete cycles in the data and determining their amplitudes. The approach is simple, reliable, easily lends itself to automation, and robust even in the presence of superimposed signals or background noise. After describing the method, its use and behavior are demonstrated on three sample histories of intracellular calcium concentration in chondrocytes exposed to fluid shear stress. The method is also applied to a more challenging data set that has had an artificial random error included. The results demonstrate that the rainflow counting algorithm identifies signal oscillations and appropriately determines their amplitudes even when superimposed or distorted by background noise. These attractive properties make rainflow counting a powerful approach for quantifying and characterizing biological time histories.
众多生物学研究都会产生时间历程数据。此类数据的特征描述可能会很困难，尤其是在存在信号噪声或叠加信号的情况下。本文介绍了几种可以采用的方法，包括快速傅里叶变换（FFT）、阈值处理、峰值计数和范围计数。然而，这些方法中的每一种都有明显的缺点。在本文中，我们描述了一种新颖的方法，称为雨流循环计数法，用于根据尖峰或振荡幅度及频率来表征随时间变化的生物学时间历程数据。雨流计数是一种用于识别数据中的完整周期并确定其幅度的直接算法。该方法简单、可靠，易于自动化，即使在存在叠加信号或背景噪声的情况下也很稳健。在描述了该方法之后，我们在暴露于流体剪切应力的软骨细胞内钙浓度的三个样本历程上展示了它的用途和性能。该方法还应用于一个包含人工随机误差的更具挑战性的数据集。结果表明，即使在被背景噪声叠加或扭曲的情况下，雨流计数算法也能识别信号振荡并适当地确定其幅度。这些吸引人的特性使雨流计数成为一种用于量化和表征生物学时间历程的强大方法。
