Nonlinear Smoothing of Data with Random Gaps and Outliers (DRAGO) Improves Estimation of Circadian Rhythm

A. Parekh, I. Ayappa, R. S. Osorio, I. W. Selesnick, A. Baroni, M. Miller, B. Cavedoni, H. Sanders, A. W. Varga, E. Blessing, D. M. Rapoport

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Core body temperature measurement using an ingestible pill has been proven effective for field-based ambulatory applications. The ingestible pill overcomes many impracticalities related with traditional methods of assessing core body temperature, however, it suffers from two key issues: random gaps due to missing data and outliers due to electromagnetic intereference. In this paper, we propose a principled convex optimization based framework for preprocessing the raw core body temperature signal. The proposed framework assumes that the raw core body temperature signal consists of two components: a smooth low-frequency component and a transient component with sparse outliers. We derive a computationally efficient algorithm using the majorization-minimization procedure and show its performance on simulated data. Finally, we demonstrate utility of the proposed method for estimating the circadian rhythm of core body temperature in cognitively normal elderly.

Original languageEnglish (US)
Title of host publication2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728143439
DOIs
StatePublished - Dec 2019
Event2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2019 - Philadelphia, United States
Duration: Dec 7 2019 → …

Publication series

Name2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2019 - Proceedings

Conference

Conference2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2019
CountryUnited States
CityPhiladelphia
Period12/7/19 → …

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics

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    Parekh, A., Ayappa, I., Osorio, R. S., Selesnick, I. W., Baroni, A., Miller, M., Cavedoni, B., Sanders, H., Varga, A. W., Blessing, E., & Rapoport, D. M. (2019). Nonlinear Smoothing of Data with Random Gaps and Outliers (DRAGO) Improves Estimation of Circadian Rhythm. In 2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2019 - Proceedings [9037837] (2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPMB47826.2019.9037837