Analysis of coherence between sleep EEG and ECG signals during and after obstructive sleep apnea events

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    Author
    Khandoker, AH; Karmakar, CK; Palaniswami, M
    Date
    2008-01-01
    Source Title
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
    Publisher
    IEEE
    University of Melbourne Author/s
    KHANDOKER, AHSAN; Palaniswami, Marimuthu; Karmakar, Chandan
    Affiliation
    Electrical And Electronic Engineering
    Metadata
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    Document Type
    Conference Paper
    Citations
    Khandoker, A. H., Karmakar, C. K. & Palaniswami, M. (2008). Analysis of coherence between sleep EEG and ECG signals during and after obstructive sleep apnea events. 2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8, 2008, pp.3876-3879. IEEE. https://doi.org/10.1109/IEMBS.2008.4650056.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/33138
    DOI
    10.1109/IEMBS.2008.4650056
    ARC Grant code
    ARC/LP0454378
    Description

    F1 - Full Written Papers Refereed
    Abstract
    This study presents the first successful preliminary attempt to directly investigate the interactions of power spectra of sleep EEG and ECG signals of patients with obstructive sleep apnea syndrome (OSAS) by coherence analysis. ECG and EEG signals were collected from 8 OSAS patients and 3 healthy subjects. Coherence between two signals over different frequency bands(0-128 Hz) were calculated for normal breathing events, obstructive sleep apnea (OSA) events and events following OSA terminations (with/without arousals) in non-REM as well as REM sleep. Overall coherence of ECG and EEG in REM sleep is higher than that in non-REM sleep. A significant (p=0.0164) difference of coherence in the range of 10-5 Hz was found among normal, OSA and termination events in REM sleep. The results could be useful in detecting OSA events or OSA related arousals to characterize sleep fragmentation from ECG and EEG signals.
    Keywords
    Biomedical Engineering not elsewhere classified; Pattern Recognition and Data Mining; Rehabilitation Engineering; Biomedical Engineering not elsewhere classified; Pattern Recognition; Rehabilitation Engineering ; Medical Instrumentation; Computer Equipment; Medical Instruments; Computer and Electronic Office Equipment

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