Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach
The crosstalk between organs plays a crucial role in physiological processes. This coupling is a dynamical process, it must cope with a huge variety of rhythms with frequencies ranging from milliseconds to hours, days, seasons. The brain is a central hub for this crosstalk. During sleep, automatic r...
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2021-04-01
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doaj-e365c236107b4dad942930d872fc663f2021-04-09T06:40:26ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872021-04-01710.3389/fams.2021.624456624456Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency ApproachAlexandre GuilletAlain ArneodoFrançoise ArgoulThe crosstalk between organs plays a crucial role in physiological processes. This coupling is a dynamical process, it must cope with a huge variety of rhythms with frequencies ranging from milliseconds to hours, days, seasons. The brain is a central hub for this crosstalk. During sleep, automatic rhythmic interrelations are enhanced and provide a direct insight into organ dysfunctions, however their origin remains a difficult issue, in particular in sleep disorders. In this study, we focus on EEG, ECG, and airflow recordings from polysomnography databases. Because these signals are non-stationary, non-linear, noisy, and span wide spectral ranges, a time-frequency analysis, based on wavelet transforms, is more appropriate to handle this complexity. We design a wavelet-based extraction method to identify the characteristic rhythms of these different signals, and their temporal variability. These new constructs are combined in pairs to compute their wavelet-based time-frequency complex coherence. These time-frequency coherence maps highlight the occurrence of a slowly modulated coherence pattern in the frequency range [0.01–0.06] Hz, which appears in both obstructive and central apnea. A preliminary exploration of a large database from the National Sleep Research Resource with respiration disorders, such as apnea provides some clues on its relation with autonomic cardio-respiratory coupling and brain rhythms. We also observe that during sleep apnea episodes (either obstructive or central), the cardiopulmonary coherence (in particular respiratory sinus-arrhythmia) in the frequency range [0.1–0.7] Hz strongly diminishes, suggesting a modification of this coupling. Finally, comparing time-averaged coherence with heart rate variability spectra in different apnea episodes, we discuss their common trait and their differences.https://www.frontiersin.org/articles/10.3389/fams.2021.624456/fulltime-frequency analysiscorrelationwavelet coherenceelectrocardiogramelectroencephalogrambreath |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alexandre Guillet Alain Arneodo Françoise Argoul |
spellingShingle |
Alexandre Guillet Alain Arneodo Françoise Argoul Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach Frontiers in Applied Mathematics and Statistics time-frequency analysis correlation wavelet coherence electrocardiogram electroencephalogram breath |
author_facet |
Alexandre Guillet Alain Arneodo Françoise Argoul |
author_sort |
Alexandre Guillet |
title |
Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach |
title_short |
Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach |
title_full |
Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach |
title_fullStr |
Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach |
title_full_unstemmed |
Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach |
title_sort |
tracking rhythms coherence from polysomnographic records: a time-frequency approach |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Applied Mathematics and Statistics |
issn |
2297-4687 |
publishDate |
2021-04-01 |
description |
The crosstalk between organs plays a crucial role in physiological processes. This coupling is a dynamical process, it must cope with a huge variety of rhythms with frequencies ranging from milliseconds to hours, days, seasons. The brain is a central hub for this crosstalk. During sleep, automatic rhythmic interrelations are enhanced and provide a direct insight into organ dysfunctions, however their origin remains a difficult issue, in particular in sleep disorders. In this study, we focus on EEG, ECG, and airflow recordings from polysomnography databases. Because these signals are non-stationary, non-linear, noisy, and span wide spectral ranges, a time-frequency analysis, based on wavelet transforms, is more appropriate to handle this complexity. We design a wavelet-based extraction method to identify the characteristic rhythms of these different signals, and their temporal variability. These new constructs are combined in pairs to compute their wavelet-based time-frequency complex coherence. These time-frequency coherence maps highlight the occurrence of a slowly modulated coherence pattern in the frequency range [0.01–0.06] Hz, which appears in both obstructive and central apnea. A preliminary exploration of a large database from the National Sleep Research Resource with respiration disorders, such as apnea provides some clues on its relation with autonomic cardio-respiratory coupling and brain rhythms. We also observe that during sleep apnea episodes (either obstructive or central), the cardiopulmonary coherence (in particular respiratory sinus-arrhythmia) in the frequency range [0.1–0.7] Hz strongly diminishes, suggesting a modification of this coupling. Finally, comparing time-averaged coherence with heart rate variability spectra in different apnea episodes, we discuss their common trait and their differences. |
topic |
time-frequency analysis correlation wavelet coherence electrocardiogram electroencephalogram breath |
url |
https://www.frontiersin.org/articles/10.3389/fams.2021.624456/full |
work_keys_str_mv |
AT alexandreguillet trackingrhythmscoherencefrompolysomnographicrecordsatimefrequencyapproach AT alainarneodo trackingrhythmscoherencefrompolysomnographicrecordsatimefrequencyapproach AT francoiseargoul trackingrhythmscoherencefrompolysomnographicrecordsatimefrequencyapproach |
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