Phase Estimation from Noisy Data with Gaps
Author ORCID Identifier
Yitong Huang: 0000-0002-5200-8077
Document Type
Conference Proceeding
Publication Date
7-1-2019
Publication Title
2019 13th International Conference on Sampling Theory and Applications Sampta 2019
Abstract
Determining the phase of a rhythm embedded in a time series is a key step in understanding many oscillatory systems. While existing approaches such as harmonic regression and cross-correlation are effective even when some data are missing, we show that they can produce biased estimates of phase when missing data are consecutive (i.e., there is a gap). We propose a simple modification of the least-squares approach, Gap Orthogonalized Accelerated Least Squares (GOALS), which addresses this issue with a negligible increase in computational expense. We test GOALS against other approaches on a synthetic dataset and on a real-world dataset of activity recorded by an Apple Watch, showing in both cases that GOALS is effective at recovering phase estimates from noisy data with gaps.
DOI
10.1109/SampTA45681.2019.9030828
Recommended Citation
Huang, Yitong; Bowman, Clark; Walch, Olivia; and Forger, Daniel, "Phase Estimation from Noisy Data with Gaps" (2019). Mathematics Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/mth_facpubs/196