Determination of Sleep Apnea Severity Using Multi-Layer Perceptron Neural Network

Abstract

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. Sleep Med Res 2020;11(2):70-76