Provide a diagnostic model using a combination of two neural network algorithms and a genetic algorithm

Introduction: Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder. Due to the lack of information about this disease, many causes of unexpected deaths have been identified. With increasing the number of patients with this disease around the world, many patients suffer apnea complications. Most of them are not treated because of the complex and costly and time-consuming polysomnography (PSG) diagnostic procedure.Material and Methods: This descriptive-analytical study was performed on ۵۰ patients referred to sleep clinic of Imam Khomeini Hospital in Tehran, Attempts to design, and develop a system for detection of sleep apnea and its severity using ECG signals, RR intervals and airflow. The random forest algorithm and MATLAB۲۰۱۶ were used in the design of the system that the algorithm inputs are extracted ۸ features nonlinear in time-frequency domain from airflow and ECG signals and ۱۰ nonlinear features of RR intervals.Results: The accuracy for normal, obstructive, central and mixed apnea was obtained at ۹۵.۳%, ۹۷.۹۲%, ۹۹.۶۰%, and ۹۷.۲۹%, respectively, and the accuracy For detection of normal, mild, moderate and severe apnea was obtained ۹۶%, ۹۴%, ۹۴%, ۹۶% respectively. According to the results, the proposed system can correctly classify the types of sleep apnea and its severity.Conclusion: The proposed system, which has high performance capability in addition to increasing the physician speed and accuracy in the diagnosis of apnea can be used in home systems and the areas where healthcare facilities are not sufficient.

Keywords:

Sleep Apnea , Polysomnography , ECG , Airflow , Random Forest