The Correlation Between Phase Coherence of Respiratory Sinus Arrhythmia and Slow Wave Brain Activity is Altered in Depressed Patients with and Without Obstructive Sleep Apnea During Sleep

Yahya Alzaabi, Mostafa M. Moussa, Ahsan H. Khandoker, Name Yahya Alzaabi

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    The aim of this study is to investigate the correlation between slow wave sleep and phase coherence between respiratory sinus arrhythmia (RSA) and respiration in OSA patients with and without major depressive disorder (MDD), and how it differs from healthy subjects. Overnight electroencephalogram (EEG), electrocardiograms (ECG), and breathing using plethysmography were recorded from control subjects (17 CONT), OSA subjects with MDD (17 OSAMDD+) and OSA subjects without MDD (17 OSAMDD-). Slow wave activity was computed by the amplitude envelope of the EEG delta-wave(0.5-4 Hz). The interbeat intervals (RRI) and respiratory movement were extracted from ECG. RRI and respiration were resampled at 10Hz, and the band passed filtered (0.10-0.4 Hz) before the Hilbert transform was used to extract instantaneous phases of the RSA and respiration. Then, the phase coherence (lambda) between RSA and respiration was quantified. Using cross-correlation, we found that overnight profiles of lambda and delta-wave were correlated in only CONT group, with significant cross-correlation coefficient. This coefficient correlation was significantly higher than that obtained for OSAMDD+ and OSAMDD-groups. Our results suggest that the association between lambda and delta-wave can be used as trait marker for distinguishing between healthy and OSA patients.

    Original languageBritish English
    Title of host publicationComputing in Cardiology, CinC 2023
    PublisherIEEE Computer Society
    ISBN (Electronic)9798350382525
    DOIs
    StatePublished - 2023
    Event50th Computing in Cardiology, CinC 2023 - Atlanta, United States
    Duration: 1 Oct 20234 Oct 2023

    Publication series

    NameComputing in Cardiology
    ISSN (Print)2325-8861
    ISSN (Electronic)2325-887X

    Conference

    Conference50th Computing in Cardiology, CinC 2023
    Country/TerritoryUnited States
    CityAtlanta
    Period1/10/234/10/23

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