A Bayesian approach for periodic components estimation for chronobiological signals

Abstract : The toxicity and efficacy of more than 30 anticancer agents presents very high variations, depending on the dosing time. Therefore the biologists studying the circadian rhythm require a very precise method for estimating the Periodic Components (PC) vector of chronobiological signals. Moreover, in recent developments not only the dominant period or the PC vector present a crucial interest, but also their stability or variability. In cancer treatment experiments the recorded signals corresponding to different phases of treatment are short, from seven days for the synchronization segment to two or three days for the after treatment segment. When studying the stability of the dominant period we have to consider very short length signals relative to the prior knowledge of the dominant period, placed in the circadian domain. The classical approaches, based on Fourier Transform (FT) methods are inefficient (i.e. lack of precision) considering the particularities of the data (i.e. the short length). In this thesis we propose a new method for the estimation of the PC vector of biomedical signals, using the biological prior informations and considering a model that accounts for the noise. The experiments developed in the cancer treatment context are recording signals expressing a limited number of periods. This is a prior information that can be translated as the sparsity of the PC vector. The proposed method considers the PC vector estimation as an Inverse Problem (IP) using the general Bayesian inference in order to infer all the unknowns of our model, i.e. the PC vector but also the hyperparameters.
Complete list of metadatas

Cited literature [149 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-01318048
Contributor : Abes Star <>
Submitted on : Thursday, May 19, 2016 - 10:49:07 AM
Last modification on : Thursday, July 11, 2019 - 3:24:45 AM

File

76211_DUMITRU_2016_diffusion.p...
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01318048, version 1

Citation

Mircea Dumitru. A Bayesian approach for periodic components estimation for chronobiological signals. Probability [math.PR]. Université Paris-Saclay, 2016. English. ⟨NNT : 2016SACLS104⟩. ⟨tel-01318048⟩

Share

Metrics

Record views

687

Files downloads

689