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Sleep staging using cardiorespiratory signals

Schlafstadien unter Verwendung von kardiorespiratorischen Signalen

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Somnologie - Schlafforschung und Schlafmedizin Aims and scope Submit manuscript

Zusammenfassung

Fragestellung

Vor kurzem untersuchten wir die Möglichkeit, reduzierte Schlaf- Wach-REM Informationen bei Patienten mit Verdacht auf Schlafapnoe zu erhalten, dabei benutzten wir nur EKG und Atmungssignale. Der Nutzen eines solchen Systems kann dadurch beeinträchtigt sein, dass sich unter den Patienten Personen mit OSAS (in verschiedener Ausprägung) befinden. Die vorliegende Studie überprüft die Effektivität dieses Systems bei einer Personengruppe ohne schlafbezogene Atmungsstörungen.

Patienten und Methode

Die Studie untersuchte 31 männliche Personen (Alter = 42.0 ± 7.4 Jahre, BMI = 30.7 ± 3.0 kg/m2). Es lagen keine schlafbezogenen Atmungsstörungen bei den Probanden (AHI = 1.4 ± 1.2 Fälle/Stunde) vor. Es wurde bei jedem eine Polysomnographie mit EEG, submentalen EMG und EOG durchgeführt. Ein automatisches Schlafphasenerkennungssystem, basierend auf einem einzelnen EKG Signal und einem Atmungssignal (Induktionsplethysmographie), wurde entwickelt. Parameter zur Unterscheidung der Schlafzustände wurden abgeleitet und die Leistung einer linearen und quadratischen Diskriminanzanalyse bezogen auf eine epochenweise Schlafstadienklassifikation bestimmt. Der Einsatz einer zeitabhängigen A-Priori-Wahrscheinlichkeit im Klassifizierungsmodell wurde auch untersucht.

Ergebnisse

Das beste Ergebnis erzielte ein lineares diskriminantes Klassifizierungsmodell unter Einsatz einer zeitabhängigen A-Priori Wahrscheinlichkeit. Für ein 3-Kategorien- System (W, S, R) wurde eine Übereinstimmung mit κ = 0.45 gefunden, welche sich auf κ = 0.57 erhöht, wenn ein einfaches 2-Kategorien-System (W, S/R) betrachtet wurde. Dies entspricht einer Genauigkeit von 89% bei einer Schlaf-Wach-Klassifikation.

Schlussfolgerung

Kardiorespiratorische Signale können eine Schlaf-Wach-Phasenerkennung liefern, welche mit dem Aktigraph vergleichbar ist. Das Vorhandensein oder Fehlen schlafbezogener Atmungsstörungen verändert die Klassifizierungsgenauigkeit nur unwesentlich. Eine kardiorespiratorisch basierte Schlafphasenerkennung kann eine nützliche Ergänzung zur ambulanten Schlafapnoe- Untersuchung sein.

Summary

Question of study

We recently investigated the possibility of obtaining simplified Sleep- Wake-REM sleep stage information from subjects being assessed for Obstructive Sleep Apnea Syndrome (OSAS), using only electrocardiogram and respiration signals. The utility of such a system may be limited somewhat by the presence of OSAS in the patient group (in various degrees of severity). This study examines the effectiveness of such a system when applied to a subject group in which Sleep Disordered Breathing (SDB) is absent.

Patients and methods

The study examined a database of 31 male subjects (Age = 42.0 ± 7.4 years, BMI = 30.7 ± 3.0 kg/m2). There was no significant presence of SDB in any of the subjects (AHI = 1.4 ± 1.2 events/h). A full polysomnography recording was obtained for each subject, including EEG, submental EMG and EOG for sleep staging.An automated sleep-staging system based solely on a single electrocardiogram signal and an inductance plethysmogram estimate of respiratory effort was developed. Features providing useful discrimination of sleep states were derived and the performance of both linear and quadratic discriminant classifiers were compared in correctly labeling 30-second epochs. The use of a time-dependent a priori probability in the classifier models was also investigated.

Results

The best performance obtained was achieved by a linear discriminant classifier model using a time-dependent a priori probability. For a 3-class (W, S, R) system an agreement of κ = 0.45 was seen,which increases to κ = 0.57 when a simplified 2-class (W, S/R) system is considered. This corresponds to an epoch sleep-wake classification accuracy of 89%.

Conclusions

Cardiorespiratory signals can provide sleep-wake staging accuracy which is comparable to actigraphy. Classification accuracy is not significantly altered by the presence or absence of sleep disturbed breathing. Cardiorespiratory-based sleep staging may be a useful addition to home sleep apnea monitoring systems.

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Authors and Affiliations

  1. School of Electrical, Electronic and Mechanical Engineering, University College Dublin, Room 146, Belfield Dublin 4, Ireland

    S. J. Redmond, C. O'Brien & C. Heneghan

  2. BiancaMed Ltd., Dublin, Ireland

    P. de Chazal & C. Heneghan

  3. The Respiratory Sleep Disorders Unit, St.Vincent's University Hospital, Dublin, Ireland

    S. Ryan & W. T. McNicholas

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  1. S. J. Redmond
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  2. P. de Chazal
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  3. C. O'Brien
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  5. W. T. McNicholas
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  6. C. Heneghan
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Correspondence to S. J. Redmond.

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Redmond, S.J., de Chazal, P., O'Brien, C. et al. Sleep staging using cardiorespiratory signals. Somnologie 11, 245–256 (2007). https://doi.org/10.1007/s11818-007-0314-8

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