Uncertainty and information in physiological signals: Explicit physical trade-off with log-normal wavelets - Archive ouverte HAL
Article Dans Une Revue Journal of The Franklin Institute Année : 2024

Uncertainty and information in physiological signals: Explicit physical trade-off with log-normal wavelets

Résumé

Physiological recordings contain a great deal of information about the underlying dynamics of Life. The practical statistical treatment of these single-trial measurements is often hampered by the inadequacy of overly strong assumptions. Heisenberg's uncertainty principle allows for more parsimony, trading off statistical significance for localization. By decomposing signals into time-frequency atoms and recomposing them into local quadratic estimates, we propose a concise and expressive implementation of these fundamental concepts based on the choice of a geometric paradigm and two physical parameters. Starting from the spectrogram based on two fixed timescales and Gabor's normal window, we then build its scale-invariant analogue, the scalogram based on two quality factors and Grossmann's log-normal wavelet. These canonical estimators provide a minimal and flexible framework for single trial time-frequency statistics, which we apply to polysomnographic signals: EEG representations, HRV extraction from ECG, coherence and mutual information between heart rate and respiration.
Fichier principal
Vignette du fichier
Guillet_JFrankInstit2024.pdf (4.42 Mo) Télécharger le fichier
Origine Publication financée par une institution

Dates et versions

hal-04700580 , version 1 (17-09-2024)

Licence

Identifiants

Citer

Alexandre Guillet, Françoise Argoul. Uncertainty and information in physiological signals: Explicit physical trade-off with log-normal wavelets. Journal of The Franklin Institute, 2024, 361 (18), pp.107201. ⟨10.1016/j.jfranklin.2024.107201⟩. ⟨hal-04700580⟩

Collections

CNRS LOMA ANR
0 Consultations
0 Téléchargements

Altmetric

Partager

More