DETECTION AND CHARACTERIZATION OF PEAKS AND ESTIMATION OF INSTANTANEOUS SECRETORY RATE FOR EPISODIC PULSATILE HORMONE-SECRETION
DETECTION AND CHARACTERIZATION OF PEAKS AND ESTIMATION OF INSTANTANEOUS SECRETORY RATE FOR EPISODIC PULSATILE HORMONE-SECRETION
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DOI：
10.1016/0010-4809(86)90014-5
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发表时间：
1986-04-01
期刊：
影响因子：
--
通讯作者：
RODBARD, D
中科院分区：
文献类型：
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作者：
OERTER, KE;GUARDABASSO, V;RODBARD, D
我们已经开发了一种新的计算机程序，用于检测“峰值”在连续的激素测量的纵向研究，情节激素分泌。该方案提供：（a）估计作为激素水平的函数的随机测量误差的几种基于统计的方法;（B）基于一阶导数的分析的峰值检测，该分析具有针对具有指数衰减的不对称峰值而优化的逻辑;（c）估计一阶和二阶导数的容限的几种方法;（d）灵敏的曲线拟合方法，以区分上升、指数衰减和平坦基线;（e）能够检测多个重叠峰值;（f）通过围绕最可能的值系统地改变阈值来分析稳健性;（g）叠加检测到的峰，以评估平均峰形;（h）分析衰减速率，以获得消失速率常数和半衰期的估计值;（i）使用离散去卷积方法，以求解分泌的瞬时速率，并提供误差分析，以获得这些导出值的精度的估计值;以及（j）与其他相关系列的相关性，作为交叉验证的手段。该程序已在真实的和合成数据上进行了广泛的测试，似乎表现良好。假阳性峰值的频率可以保持在任何期望的低水平，并且可以防止随着采样频率的增加而增加。任意假设、近似值或阈值的数量保持在绝对最小值。这些方法是自然的，逻辑的，并遵循这一原则的统计。
We have developed a new computer program for detection of "peaks" in sequential hormone measurements in longitudinal studies of episodic hormone secretion. The program provides: (a) several stastistically based approaches to the estimation of the random measurement error as a function of hormone level; (b) peak detection based on analysis of first derivatives with logic that has been optimized for asymmetrical peaks with exponential decays; (c) several approaches to the estimation of tolerance for the first and second derivatives; (d) a sensitive curve-fitting approach, to distinguish between upstrokes, exponential decays, and flat baselines; (e) ability to detect multiple overlapping peaks; (f) analysis of robustness by systematically varying the thresholds around the most-likely value; (g) superimposition of detected peaks, to evalaute average peak shape; (h) analysis of the decay rate, to obtain an estimate of the disappearance rate constant and half-life; (i) use of a discrete deconvolution approach, to solve for the aparent instantaneous rate of secretion, and provision of an error analysis to obtain estimates of the precision of these derived values; and (j) correlation with other relevant series as a means of cross validating. The program has been tested extensively on real and synthetic data, and appears to perform well. The frequency of false positive peaks can be held at any desired low level, and can be prevented from increasing as sampling frequency increases. The number of arbitrary assumptions, approximations, or thresholds is held to an absolute minimum. These methods are natural, logical, and follow from this principles of statistics.