ABSTRACT

We report herein the application of an adaptive notch filter (ANF) algorithm to minute-by-minute actigraphy data to estimate the continuous circadian phase of eight healthy adults. As the adaptation rates and damping factor of the ANF algorithm have large impacts on the ANF states and circadian phase estimation results, we propose a method for optimizing these parameters. The ANF with optimal parameters is further used to estimate the circadian phase shift from the actigraphy data. Dim light melatonin onset (DLMO), considered the “gold standard” method for identification of circadian phase, was determined by a serial collection of salivary samples analyzed for melatonin per standard protocol simultaneously with the collection of actigraphic data. We demonstrate our ANF algorithm, when applied to the actigraphy data, is able to estimate the circadian phase as determined by the DLMO. These results demonstrate that applying our ANF with a well-defined parameter tuning process to actigraphic data can provide accurate measurements of the circadian phase and its shift without resorting to salivary melatonin collections.

摘要

我们在这里报告了自适应陷波滤波器（ANF）算法在逐分钟的活动记录数据上的应用，以估算八个健康成年人的连续昼夜节律阶段。由于ANF算法的自适应率和阻尼因子对ANF状态和昼夜节律相位估计结果有较大影响，因此提出了一种优化这些参数的方法。具有最佳参数的ANF进一步用于根据书法数据估算昼夜节律相移。昏暗的褪黑激素发作（DLMO），被认为是识别昼夜节律的“黄金标准”方法，是通过一系列唾液样本的收集来确定的，该唾液样本是按照标准方案分析褪黑激素的同时收集活性数据的。我们展示了将ANF算法应用于书法数据时，能够估计DLMO确定的昼夜节律相位。这些结果表明，将我们的具有明确定义的参数调整过程的ANF应用于活动记录数据，可以在不求助唾液褪黑激素收集的情况下，准确测量昼夜节律及其相移。