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WAVE-SHAPE OSCILLATORY MODEL FOR NONSTATIONARY PERIODIC TIME SERIES ANALYSIS

Publication ,  Journal Article
Lin, YT; Malik, J; Wu, HT
Published in: Foundations of Data Science
June 1, 2021

The oscillations observed in many time series, particularly in biomedicine, exhibit morphological variations over time. These morphological variations are caused by intrinsic or extrinsic changes to the state of the generating system, henceforth referred to as dynamics. To model these time series (including and specifically pathophysiological ones) and estimate the underlying dynamics, we provide a novel wave-shape oscillatory model. In this model, time-dependent variations in cycle shape occur along a manifold called the wave-shape manifold. To estimate the wave-shape manifold associated with an oscillatory time series, study the dynamics, and visualize the time-dependent changes along the wave-shape manifold, we propose a novel algorithm coined Dynamic Diffusion map (DDmap) by applying the well-established diffusion maps (DM) algorithm to the set of all observed oscillations. We provide a theoretical guarantee on the dynamical information recovered by the DDmap algorithm under the proposed model. Applying the proposed model and algorithm to arterial blood pressure (ABP) signals recorded during general anesthesia leads to the extraction of nociception information. Applying the wave-shape oscillatory model and the DDmap algorithm to cardiac cycles in the electrocardiogram (ECG) leads to ectopy detection and a new ECG-derived respiratory signal, even when the subject has atrial fibrillation.

Duke Scholars

Published In

Foundations of Data Science

DOI

EISSN

2639-8001

Publication Date

June 1, 2021

Volume

3

Issue

2

Start / End Page

99 / 131
 

Citation

APA
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MLA
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Lin, Y. T., Malik, J., & Wu, H. T. (2021). WAVE-SHAPE OSCILLATORY MODEL FOR NONSTATIONARY PERIODIC TIME SERIES ANALYSIS. Foundations of Data Science, 3(2), 99–131. https://doi.org/10.3934/fods.2021009
Lin, Y. T., J. Malik, and H. T. Wu. “WAVE-SHAPE OSCILLATORY MODEL FOR NONSTATIONARY PERIODIC TIME SERIES ANALYSIS.” Foundations of Data Science 3, no. 2 (June 1, 2021): 99–131. https://doi.org/10.3934/fods.2021009.
Lin YT, Malik J, Wu HT. WAVE-SHAPE OSCILLATORY MODEL FOR NONSTATIONARY PERIODIC TIME SERIES ANALYSIS. Foundations of Data Science. 2021 Jun 1;3(2):99–131.
Lin, Y. T., et al. “WAVE-SHAPE OSCILLATORY MODEL FOR NONSTATIONARY PERIODIC TIME SERIES ANALYSIS.” Foundations of Data Science, vol. 3, no. 2, June 2021, pp. 99–131. Scopus, doi:10.3934/fods.2021009.
Lin YT, Malik J, Wu HT. WAVE-SHAPE OSCILLATORY MODEL FOR NONSTATIONARY PERIODIC TIME SERIES ANALYSIS. Foundations of Data Science. 2021 Jun 1;3(2):99–131.

Published In

Foundations of Data Science

DOI

EISSN

2639-8001

Publication Date

June 1, 2021

Volume

3

Issue

2

Start / End Page

99 / 131