A Signal Extraction Approach to Modeling Hormone Time Series with Pulses and a Changing Baseline
含脉冲与变化基线的激素时间序列信号提取方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接使用动态线性模型、Kalman滤波与时间序列信号提取；分类依据是方法，应用对象为生物数据。
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含脉冲与变化基线的激素时间序列信号提取方法
- 研究问题
- 激素浓度同时受脉冲式分泌与昼夜节律影响时，如何从观测时间序列中分离缓慢变化的基线和潜在脉冲成分？
- 方法与思路
- 作者采用加性结构，以平滑样条表示基线、多过程动态线性模型表示脉冲，用多过程Kalman滤波联合估计并近似最大化似然。
- 材料陈述的贡献
- 摘要称可估计脉冲位置与幅度的后验均值，并构造基线的贝叶斯区间；模拟和激素序列分析展示了方法表现。
- 阅读关注点
- 应核对基线与脉冲的可区分性、动态模型设定、近似似然误差，以及区间构造与模拟稳健性所对应的扰动范围。
- 阅读边界
- 仅据OpenAlex索引摘要，稿件版本未注明；摘要未给出识别条件或模拟数值，生物序列中的效果不能直接推广至宏观数据。
原文摘要
Hormones serve as regulating signals for many biological processes. In recent years, it was determined that many hormones are secreted in a pulsatile manner and that the pulsatile secretion pattern, in addition to the absolute concentration level, is important in regulating biological processes. Consequently, it is necessary to characterize the latent secretion patterns from measurements of concentration levels. The characterization is complicated by the presence of a biological circadian rhythm. When hormone concentrations are plotted over time, the resultant time series usually exhibits occasional short rises superimposed on a slowly changing baseline. This is a result of a mixture of pulsatile secretions and a circadian rhythm. In this article we present a signal extraction approach to model simultaneously a slowly changing component and a pulsatile component of a time series. A smoothing spline is used to model the baseline, and a multiprocess dynamic linear model is used to model the pulsatile component. An additive structure is assumed, and both components are estimated simultaneously using a multiprocess Kalman filter. The unknown parameters are estimated by approximate maximum likelihood. The locations and amplitudes of the pulses are also estimated as posterior means via the multiprocess Kalman filter. Bayesian confidence intervals can be constructed for the baseline. This approach is found to be robust in simulated data and effective in modeling hormone time series.