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ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern

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Abstract

The diagnosis of sleep-disordered breathing (SDB) usually relies on the analysis of complex polysomnographic measurements performed in specialized sleep centers. Automatic signal analysis is a promising approach to reduce the diagnostic effort. This paper addresses SDB and sleep assessment solely based on the analysis of a single-channel ECG recorded overnight by a set of signal analysis modules. The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail. SDB analysis detects specific cyclical variations of the heart rate by correlation analysis of a signal pattern and the heart rate curve. It was tested with 35 SDB-annotated ECGs from the Apnea-ECG Database, and achieved a diagnostic accuracy of 80.5%. To estimate sleep pattern, spectral parameters of the heart rate are used as stage classifiers. The reliability of the algorithm was tested with 18 ECGs extracted from visually scored polysomnographies of the SIESTA database; 57.7% of all 30 s epochs were correctly assigned by the algorithm. Although promising, these results underline the need for further testing in larger patient groups with different underlying diseases.

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Authors and Affiliations

  1. Department for Internal Medicine, Section Respiratory Diseases, Faculty of Medicine, Philipps-University Marburg, Baldingerstr. 1, 35043, Marburg, Germany

    K. Kesper, S. Canisius, T. Ploch & W. Cassel

  2. Charité Universitaetsmedizin Berlin, Interdisziplinaeres Schlafmedizinisches Zentrum, Berlin, Germany

    T. Penzel

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  1. K. Kesper
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  2. S. Canisius
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  3. T. Penzel
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  4. T. Ploch
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  5. W. Cassel
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Correspondence to K. Kesper.

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Kesper, K., Canisius, S., Penzel, T. et al. ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern. Med Biol Eng Comput 50, 135–144 (2012). https://doi.org/10.1007/s11517-011-0853-9

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  • DOI: https://doi.org/10.1007/s11517-011-0853-9

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