Our Penalized Multi-band Learning (PML) algorithm provides a novel method for studying circadian rhythm analysis using actigraphy. Specifically, it first applies Fast Fourier Transform to actigraph data and then uses shrinkage in machine learning to select dominant periodicities, the information from which is further used to characterize daily activity patterns.
Details can be found in the manuscript: Li, X., Kane, M., Zhang, Y., Sun, W., Song, Y., Dong, S., Lin, Q., Zhu, Q., Jiang, F., Zhao, H. (2019) A Novel Penalized Multi-band Learning Approach Characterizes the Consolidation of Sleep-Wake Circadian Rhythms During Early Childhood Development. Submitted.
Please see the package vignettes for more information.
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