Introduction: Among sleep-related disorders, Sleep apnea has been under more attention and it’s the most common respiratory disorder in which respiration ceases frequently which can lead to serious health disorders and even mortality. Polysomnography is the standard method for diagnosing this disease at the moment which is costly and time-consuming. The aim of the present study was to analyze essential signals for the diagnosis of sleep apnea.
Method: This analytical–descriptive was conducted on 50 patients (11 normal, 13 mild, 17 moderate and 9 severe patients) in the sleep clinic of Imam Khomeini hospital. Initially, data pre-processing was carried out in two steps(and Moving Average algorihtm). Next, using the SVD method, 12 features were extracted for airflow. Finally, to classify data, SVM with Quadratic, Polynomia and RBF kernels were trained and tested.
Results: After applying different kernel functions on SVM, the RBF kernel showed the most efficient performance. After running the RBF kernel function ten times, the mean accuracy obtained for normal, apnea, and hypopnea modes were 92.74%, 91.70%, 93.26%.
Conclusion: The results indicate that in online applications or applications in which volume and time calculations and the result are important simultaneously, patients could be diagnosed with acceptable accuracy using machine learning algorithms.
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