Designing an Intelligent System for Diagnosing Type of Sleep Apnea and Determining Its Severity

Zeinab Kohzadi,Khosro Sadeghniiat Haghighi,Reza Safdari

doi:10.30699/fhi.v10i1.287

Zeinab Kohzadi, Khosro Sadeghniiat Haghighi + Show 1 more

Open Access

https://doi.org/10.30699/fhi.v10i1.287

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Abstract

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 50 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 MATLAB2016 were used in the design of the system that the algorithm inputs are extracted 8 features nonlinear in time-frequency domain from airflow and ECG signals and 10 nonlinear features of RR intervals.Results: The accuracy for normal, obstructive, central and mixed apnea was obtained at 95.3%, 97.92%, 99.60%, and 97.29%, respectively, and the accuracy For detection of normal, mild, moderate and severe apnea was obtained 96%, 94%, 94%, 96% 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.

Highlights

Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder
The proposed system can correctly classify the types of sleep apnea and its severity
Among the matrix data obtained in the previous step, 70% were selected for training and 30% were selected for testing random forest algorithm

Summary

Introduction

Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder. 25% to 50% reduction in airflow during respiration, associated with a severe reduction in oxygen saturation, is called hypopnea [3] This disorder is manifested as central respiratory failure in 36% of patients, obstructive apnea in 12% of patients, and a combination of abnormal central and obstructive breathing in other cases [4]. Polysomnography (PSG) is the standard tool for the diagnosis of apnea at the present time In this method, sensitive receptors that are attached to the patient's body are monitored and provide important information by recording various activities such as electroencephalogram, electrooculogram, electromyography, electrocardiogram, oximetry, airflow and respiratory activities [14,15,16].

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