Actigraphy-based parameter tuning process for adaptive notch filter and circadian phase shift estimation

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.