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In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability

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dc.contributor.authors Bozkurt, MR; U?ar, MK; Bozkurt, F; Bilgin, C;
dc.date.accessioned 2020-02-27T07:01:17Z
dc.date.available 2020-02-27T07:01:17Z
dc.date.issued 2019
dc.identifier.citation Bozkurt, MR; U?ar, MK; Bozkurt, F; Bilgin, C; (2019). In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE, 42, 979-959
dc.identifier.issn 0158-9938
dc.identifier.uri https://doi.org/10.1007/s13246-019-00796-9
dc.identifier.uri https://hdl.handle.net/20.500.12619/64917
dc.description.abstract Obstructive sleep apnea is a disease that occurs in connection to pauses in respiration during sleep. Detection of the disease is achieved using a polysomnography device, which is the gold standard in diagnosis. Diagnosis is made by the steps of sleep staging and respiration scoring. Respiration scoring is performed with at least four signals. Technical knowledge is required for attaching the electrodes. Additionally, the electrodes are disturbing to an extent that will delay the patient's sleep. It is needed to have systems as alternatives for polysomnography devices that will bring a solution to these issues. This study proposes a new approach for the process of respiration scoring which is one of the diagnostic steps for the disease. A machine-learning-based apnea detection algorithm was developed for the process of respiration scoring. The study used Photoplethysmography (PPG) signal and Heart Rate Variability (HRV) that is derived from this signal. The PPG records obtained from the patient and control groups were cleaned out using a digital filter. Then, the HRV parameter was derived from the PPG signal. Later, 46 features were derived from the PPG signal and 40 features were derived from the HRV. The derived features were classified with reduced machine-learning techniques using the F-score feature-selection algorithm. The evaluation was made in a multifaceted manner. Besides, Principal Component Analysis was performed to reduce system input (features). According to the results, if a real-time embedded system is designed, the system can operate with 16 PPG feature 95%, four PPG feature 93.81% accuracy rate. These success rates are highly sufficient for the system to work. Considering all these values, it is possible to realize a practical respiration scoring system. With this study, it was agreed upon that PPG signal may be used in the diagnosis of this disease by processing it with machine learning and signal processing techniques.
dc.language English
dc.publisher SPRINGER
dc.subject Engineering
dc.title In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability
dc.type Article
dc.identifier.volume 42
dc.identifier.startpage 959
dc.identifier.endpage 979
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği Bölümü
dc.contributor.saüauthor Bozkurt, Mehmet Recep
dc.contributor.saüauthor Uçar, Muhammed Kürşad
dc.contributor.saüauthor Bozkurt, Ferda
dc.contributor.saüauthor Bilgin, Cahit
dc.relation.journal AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE
dc.identifier.wos WOS:000502445700007
dc.identifier.doi 10.1007/s13246-019-00796-9
dc.identifier.eissn 1879-5447
dc.contributor.author Bozkurt, Mehmet Recep
dc.contributor.author Uçar, Muhammed Kürşad
dc.contributor.author Bozkurt, Ferda
dc.contributor.author Bilgin, Cahit


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