지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 70 - 76 (7page)
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초록· 키워드
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Background and ObjectiveaaSleep apnea is a rather common illness, which occurs due to dyspnea during night sleep. The effects of this illness can cause problems in the patient’s life and affect its quality. Therefore, its timely diagnosis, using machine algorithms can be an important step towards preventing and controlling this illness. MethodsaaIn this study is using artificial neural networks, in order to detect the severity of sleep apnea among 200 patients, who visited the Imam Khomeini sleep clinic in Tehran. Then the artificial neural network with the structure (8-10-3-1), Sigmoid transfer function and 120 educational cycles were designed and educated based on 70% of the data at hand. The artificial neural network was designed, using MATLAB2018. ResultsaaUsing the multi-layer perceptron classifier with 10-fold cross validation tests led to 96.5%, 92.4%, 91.5% and 94.5% correctness, respectively for normal, mild, moderate and severe classifications. Enough correctness of the algorithm reduces the patients’ need to take the polysomnography test. ConclusionsaaThe results show that using artificial neural network can be useful in detecting the sleep apnea severity, without using costly tests and limited PSG.
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