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Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques

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dc.contributor.authors Ucar, MK; Bozkurt, MR; Bilgin, C; Polat, K;
dc.date.accessioned 2020-02-27T08:26:58Z
dc.date.available 2020-02-27T08:26:58Z
dc.date.issued 2017
dc.identifier.citation Ucar, MK; Bozkurt, MR; Bilgin, C; Polat, K; (2017). Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques. NEURAL COMPUTING & APPLICATIONS, 28, 2945-2931
dc.identifier.issn 0941-0643
dc.identifier.uri https://doi.org/10.1007/s00521-016-2617-9
dc.identifier.uri https://hdl.handle.net/20.500.12619/65903
dc.description.abstract Obstructive sleep apnea is a syndrome which is characterized by the decrease in air flow or respiratory arrest depending on upper respiratory tract obstructions recurring during sleep and often observed with the decrease in the oxygen saturation. The aim of this study was to determine the connection between the respiratory arrests and the photoplethysmography (PPG) signal in obstructive sleep apnea patients. Determination of this connection is important for the suggestion of using a new signal in diagnosis of the disease. Thirty-four time-domain features were extracted from the PPG signal in the study. The relation between these features and respiratory arrests was statistically investigated. The Mann-Whitney U test was applied to reveal whether this relation was incidental or statistically significant, and 32 out of 34 features were found statistically significant. After this stage, the features of the PPG signal were classified with k-nearest neighbors classification algorithm, radial basis function neural network, probabilistic neural network, multilayer feedforward neural network (MLFFNN) and ensemble classification method. The output of the classifiers was considered as apnea and control (normal). When the classifier results were compared, the best performance was obtained with MLFFNN. Test accuracy rate is 97.07 % and kappa value is 0.93 for MLFFNN. It has been concluded with the results obtained that respiratory arrests can be recognized through the PPG signal and the PPG signal can be used for the diagnosis of OSA.
dc.language English
dc.publisher SPRINGER
dc.subject Computer Science
dc.title Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques
dc.type Article
dc.identifier.volume 28
dc.identifier.startpage 2931
dc.identifier.endpage 2945
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği Bölümü
dc.contributor.saüauthor Uçar, Muhammed Kürşad
dc.contributor.saüauthor Bozkurt, Mehmet Recep
dc.contributor.saüauthor Bilgin, Cahit
dc.identifier.wos WOS:000426865100009
dc.identifier.doi 10.1007/s00521-016-2617-9
dc.identifier.eissn 1433-3058
dc.contributor.author Uçar, Muhammed Kürşad
dc.contributor.author Bozkurt, Mehmet Recep
dc.contributor.author Bilgin, Cahit
dc.contributor.author Kemal Polat

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