Automated sleep stage classification based on tracheal body sound and actigraphy.

Automated sleep stage classification based on tracheal body sound and actigraphy. Ger Med Sci. 2019;17:Doc02 Authors: Kalkbrenner C, Brucher R, Kesztyüs T, Eichenlaub M, Rottbauer W, Scharnbeck D Abstract The current gold standard for assessment of most sleep disorders is the in-laboratory polysomnography (PSG). This approach produces high costs and inconveniences for the patients. An accessible and simple preliminary screening method to diagnose the most common sleep disorders and to decide whether a PSG is necessary or not is therefore desirable. A minimalistic type-4 monitoring system which utilized tracheal body sound and actigraphy to accurately diagnose the obstructive sleep apnea syndrome was previously developed. To further improve the diagnostic ability of said system, this study aims to examine if it is possible to perform automated sleep staging utilizing body sound to extract cardiorespiratory features and actigraphy to extract movement features. A linear discriminant classifier based on those features was used for automated sleep staging using the type-4 sleep monitor. For validation 53 subjects underwent a full-night screening at Ulm University Hospital using the developed sleep monitor in addition to polysomnography. To assess sleep stages from PSG, a trained technician manually evaluated EEG, EOG, and EMG recordings. The classifier reached 86.9% accuracy and a Kappa of 0.69 for sleep/wake classification, 76.3% accura...
Source: GMS German Medical Science - Category: General Medicine Tags: Ger Med Sci Source Type: research