Publisher DOI: 10.22489/CinC.2018.104
Title: Detecting Respiratory Effort-Related Arousals in Polysomnographic Data Using LSTM Networks
Language: English
Authors: Schellenberger, Sven 
Shi, Kilin 
Mai, Melanie 
Wiedemann, Jan Philipp 
Steigleder, Tobias 
Eskofier, Bjorn 
Weigel, Robert 
Kölpin, Alexander  
Issue Date: Sep-2018
Source: Computing in Cardiology (2018-September): 8744065 (2018-09)
Journal or Series Name: Computing in cardiology 
Abstract (english): To diagnose sleep disorders, hours of sleep data from lots of different physiological sensors have to be analyzed. To do so, experts have to look through all the data which is time-consuming and error-prone. Automatic detection and classification of sleep related breathing disorders and arousals would significantly simplify this task. This years Physionet/CinC Challenge deals with this topic. This paper examines the use of a Long Short-Term Memory network for automatic arousal detection. On the test set, an AUPRC score of 0.14 was achieved.
ISBN: 978-172810958-9
ISSN: 0276-6574
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
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