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

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.

Fast tuning of observer-based circadian phase estimator using biometric data

“…Particularly, studies have used the dim light melatonin onset (DLMO) - the time at which the melatonin concentration crosses a certain threshold in dim lighting - as a circadian phase marker since it was first proposed by Lewy et al in [9] . This method has proven highly useful in clinical research, but DLMO can only be used in estimating the timing of phase markers or the phase shift between two points [10] . Moreover, melatonin values are not available in real-time but require laboratory processing.…”

“…These methods largely still focus on the estimation of phase markers instead of continuous estimates. Yin et al attempt to solve the continuous estimation problem in [10] by using an adaptive notch filter (ANF). Their approach is able to estimate the continuous circadian phase with appreciable accuracy, but the nonlinear system is rather complex and requires significant resources for tuning.…”

Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation

“…Although several of these approaches have been validated using PSG and self-reported sleep quality ratings under certain controlled conditions, there is ongoing research to validate the software and develop in-house algorithms for different applications and under real-world circumstances [ 29 - 31 ]. Several of the devices, in order to save memory and battery, provide preprocessed, 1-minute averaged acceleration data [ 6 , 32 - 35 ], whereas others provide continuous high-frequency data [ 36 - 38 ].…”

A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data

“…19 Another approach has used an adaptive notch filter (ANF) to derive harmonic estimates to estimate phase shifts. 20 The limit cycle, linear regression, and ANF models all require several days of data for (a) the limit cycle result to be insensitive to initial conditions or (b) to define average activity markers, limiting their utility for a phase assessment that can be performed quickly (with the goal of real-time assessment). Other regression models have used light and skin temperature recordings from multiple skin electrodes over a 24-hour period to estimate circadian phase with a mean error of 0.7 hours.…”

“…Some regression models have used derived markers of actigraphy rhythms, such as the time of peak activity or the time when wrist temperature begins to increase, to estimate DLMO 19 . Another approach has used an adaptive notch filter (ANF) to derive harmonic estimates to estimate phase shifts 20 . The limit cycle, linear regression, and ANF models all require several days of data for (a) the limit cycle result to be insensitive to initial conditions or (b) to define average activity markers, limiting their utility for a phase assessment that can be performed quickly (with the goal of real‐time assessment).…”