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Title: | Automatic Detection of Respiratory Effort Related Arousals from Polysomnographic Recordings | Authors: | Lazic I. Jakovljević, Nikša Despotovic D. Lončar-Turukalo, Tatjana |
Issue Date: | 1-Sep-2018 | Journal: | Computing in Cardiology | Abstract: | © 2018 Creative Commons Attribution. A reliable automatic categorization of respiratory effort is paramount for sleep-disordered breathing characterization from polysomnography. A respiratory effort related arousal (RERA) is a subtle breathing obstruction associated with an arousal. For identification of RERAs we focused on: chest and abdomen EMGs, airflow, and EEG; monitoring changes in ECG and SaO2. The quality of signals was assessed to overcome sensor associated problems and sporadic individual signal losses. We evaluated an ensemble learning and a deep learning approach using the engineered feature set trained on the 994 available records. The initial ensemble model was officially scored achieving a 0.081 area under the precision-recall curve (AUPRC) on a test set, whereas for the recurrent neural network model the average AUPRC was 0.295, obtained using 10-fold cross-validation. | URI: | https://open.uns.ac.rs/handle/123456789/1300 | ISBN: | 9781728109589 | ISSN: | 23258861 | DOI: | 10.22489/CinC.2018.226 |
Appears in Collections: | FTN Publikacije/Publications |
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