Uncertainty and information in physiological signals: explicit physical trade-off with log-normal wavelets
Language
en
Document de travail - Pré-publication
This item was published in
2023-10-26
English Abstract
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 ...Read more >
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 and flexible estimators, 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 normal window, we then build its scale-invariant analogue, the scalogram based on two quality factors and Grossmann 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, coherence and mutual information between ECG-derived heart rate and respiration, and their spurious statistics.Read less <
English Keywords
Time-frequency analysis
Log-normal wavelet
Uncertainty principle
Polysomnography
Physiological signals
Coherence
Statistical significance
Mutual information
Single trial time-frequency statistics
Origin
Hal imported