Clifton D K, Steiner R A
Endocrinology. 1983 Mar;112(3):1057-64. doi: 10.1210/endo-112-3-1057.
Investigation of episodic endocrine secretion has been hampered by inadequate analytical techniques for describing patterns of blood concentrations over time. Although standard time series methods, such as autocorrelation and power spectral analysis, are available, their use is limited to special cases in which rhythms are regular. To facilitate the analysis of our own episodic LH data, we have developed a process for determining the frequency and amplitude of both regular and irregular endocrine rhythms (signals) in the presence of high levels of random measurement errors (noise). This process, called cycle detection, engages an iterative, computerized procedure which scans data identifying sequential increases and decreases greater than an initial, preset threshold value. One complete cycle is defined as two increases greater than threshold separated by a decrease which is also greater than threshold. For an initial first pass estimate of frequency and amplitude, the threshold is set at 2.7 times the noise standard deviation. On the next pass, the threshold is readjusted, based on an empirically derived formula, and the data are scanned again. This process is repeated until the threshold reaches a stable value. We have tested the reliability of the cycle detection process by simulating irregular rhythm fluctuations of different frequencies, corrupted by various levels of noise and evaluating the signal characteristics with cycle detection analysis. These tests indicate that cycle detection provides excellent estimates of cycle frequency and amplitude, even with signal to noise ratios as low as 1.5. The ability of this process to analyze cyclic signals of almost any shape, with either regular or irregular rhythms, should make it a valuable tool in the hands of endocrine researchers.
对间歇性内分泌分泌的研究一直受到时间上血液浓度模式描述分析技术不足的阻碍。尽管有标准的时间序列方法，如自相关和功率谱分析，但它们仅限于节律规则的特殊情况。为便于分析我们自己的间歇性促黄体生成素（LH）数据，我们开发了一种在存在高水平随机测量误差（噪声）的情况下确定规则和不规则内分泌节律（信号）的频率和幅度的方法。这个过程称为周期检测，采用一种迭代的计算机化程序，该程序扫描数据，识别大于初始预设阈值的连续增加和减少。一个完整的周期定义为两次大于阈值的增加，中间由一次也大于阈值的减少隔开。对于频率和幅度的初始首次估计，阈值设定为噪声标准差的2.7倍。在下一次扫描时，根据经验推导的公式重新调整阈值，并再次扫描数据。这个过程重复进行，直到阈值达到稳定值。我们通过模拟不同频率的不规则节律波动，加入不同水平的噪声，并使用周期检测分析评估信号特征，测试了周期检测过程的可靠性。这些测试表明，即使信噪比低至1.5，周期检测也能很好地估计周期频率和幅度。该过程能够分析几乎任何形状、具有规则或不规则节律的周期性信号，这使其成为内分泌研究人员手中的一个有价值的工具。
