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Enhancing current cardiorespiratory-based approaches of sleep stage classification by temporal feature stacking

  • This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen’s κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.

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Author of HS ReutlingenMartínez Madrid, Natividad
Erschienen in:43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 1-5 November 2021, Mexico, proceedings
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Year of Publication:2021
Tag:biology; classification algorithms; feature extraction; measurement; rapid eye movement sleep; stacking; training data
Page Number:5
First Page:5518
Last Page:5522
DDC classes:610 Medizin, Gesundheit
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE