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Oscillatory Biomedical Signals: Frontiers in Mathematical Models and Statistical Analysis

Hau-Tieng Wu Department of Mathematics, Duke University, Durham Tze Leung Lai Department of Statistics, Stanford University, Stanford Gabriel G. Haddad 3Department of Pediatrics and Rady Children’s Hospital, University of California Alysson Muotri Department of Cellular & Molecular Medicine and Department of Pediatrics

Data Analysis, Bio-Statistics, Bio-Mathematics mathscidoc:2105.45001

2021.1
Herein we describe new frontiers in mathematical modeling and statistical analysis of oscillatory biomedical signals, motivated by our recent studies of network formation in the human brain during the early stages of life and studies forty years ago on cardiorespiratory patterns during sleep in infants and animal models. The frontiers involve new nonlinear-type time-frequency analysis of signals with multiple oscillatory components, and efficient particle filters for joint state and parameter estimators together with uncertainty quantification in hidden Markov models and empirical Bayes inference.
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@inproceedings{hau-tieng2021oscillatory,
  title={Oscillatory Biomedical Signals: Frontiers in Mathematical Models and Statistical Analysis},
  author={Hau-Tieng Wu, Tze Leung Lai, Gabriel G. Haddad, and Alysson Muotri},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20210504113802416036798},
  year={2021},
}
Hau-Tieng Wu, Tze Leung Lai, Gabriel G. Haddad, and Alysson Muotri. Oscillatory Biomedical Signals: Frontiers in Mathematical Models and Statistical Analysis. 2021. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20210504113802416036798.
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