Authors:
Belmin Alić
1
;
Tim Zauber
1
;
Chen Zhang
2
;
Wang Liao
2
;
Alina Wildenauer
3
;
Noah Leosz
3
;
Torsten Eggert
3
;
Sarah Dietz-Terjung
3
;
Sivagurunathan Sutharsan
4
;
Gerhard Weinreich
4
;
Christoph Schöbel
3
;
Gunther Notni
2
;
Christian Wiede
5
and
Karsten Seidl
1
;
5
Affiliations:
1
Faculty of Engineering, University of Duisburg-Essen, Duisburg, Germany
;
2
Department of Mechanical Engineering, Ilmenau University of Technology, Ilmenau, Germany
;
3
Center for Sleep Medicine, University Hospital Essen, Essen, Germany
;
4
Department of Pneumology, University Hospital Essen, Essen, Germany
;
5
Fraunhofer Institute for Microelectronic Circuits and Systems, Duisburg, Germany
Keyword(s):
Contactless, Optical, Apnea, Hypopnea, Respiration, Sleep, OSA, AHI, Multi-Spectral, Data Fusion.
Abstract:
Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder characterized by the collapse of the upper airway and associated with various diseases. For clinical diagnosis, a patient’s sleep is recorded during the night via polysomnography (PSG) and evaluated the next day regarding nocturnal respiratory events. The most prevalent events include obstructive apneas and hypopneas. In this paper, we introduce a fully automatic contactless optical method for the detection of nocturnal respiratory events. The goal of this study is to demonstrate how nocturnal respiratory events, such as apneas and hypopneas, can be autonomously detected through the analysis of multi-spectral image data. This represents the first step towards a fully automatic and contactless diagnosis of OSA. We conducted a trial patient study in a sleep laboratory and evaluated our results in comparison with PSG, the gold standard in sleep diagnostics. In a study sample with three patients, 24 hours of recor
ded video materials and 245 respiratory events, we have achieved a classification accuracy of 82 % with a random forest classifier.
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