ARGOUL, Françoise; GUILLET, Alexandre

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ARGOUL, Françoise
Laboratoire Ondes et Matière d'Aquitaine [LOMA]

GUILLET, Alexandre
Laboratoire Ondes et Matière d'Aquitaine [LOMA]

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Este ítem está publicado en

2023-10-26

Resumen en inglés

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.< Leer menos

Palabras clave en inglés

Time-frequency analysis

Log-normal wavelet

Uncertainty principle

Polysomnography

Physiological signals

Coherence

Statistical significance

Mutual information

Single trial time-frequency statistics

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

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