A different sleep apnea classification system with neural network based on the acceleration signals

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dc.contributor.authorYüzer, Ahmet Hayrettin
dc.contributor.authorSümbül, Harun
dc.contributor.authorNour, Majid
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:54:12Z
dc.date.available2021-06-23T19:54:12Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractBackground and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly, the disabled, and pediatric sleep apnea patients. Methods: In this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical system (MEMS) based acceleration sensor. It records the value of acceleration by measuring the movements of the diaphragm in three axes during the respiratory. The measurements are carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement results. An artificial neural network model was designed to determine the apnea event. For the number of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer. Results: A study group was formed of 5 patients (having different characteristics (age, height, and body weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then Apnea event is detected successfully for the other patients by using ANN trained only with H1's ACC data. Conclusions: The sleep apnea event detection system is presented by using ANN from directly acceleration values. Measurements are performed by the MEMS-based accelerometer and Industrial Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients. The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with 1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark shows us that trained ANN successfully detects apnea events. One of the contributions of this study to literature is that only ACC data are used in the ANN training step. After training for one patient, the ANN system can monitor the apnea event situation on-line for others. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.apacoust.2020.107225
dc.identifier.issn0003-682X
dc.identifier.issn1872-910X
dc.identifier.scopus2-s2.0-85078123042en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.apacoust.2020.107225
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10456
dc.identifier.volume163en_US
dc.identifier.wosWOS:000521507200006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Acousticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSleep Apneaen_US
dc.subjectAcceleration Sensoren_US
dc.subjectAcceleration Dataen_US
dc.subjectArtificial Neural Networken_US
dc.subjectMedical Decision Makingen_US
dc.titleA different sleep apnea classification system with neural network based on the acceleration signalsen_US
dc.typeArticleen_US

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