Developing an apnea-hypopnea diagnostic model using SVM

Zeinab Kohzadi,Reza Safdari,Khosro Sadeghniiat Haghighi

doi:10.30699/fhi.v10i1.237

Zeinab Kohzadi, Reza Safdari + Show 1 more

Open Access

https://doi.org/10.30699/fhi.v10i1.237

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Abstract

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.

Highlights

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
The results show that in online applications or applications where the volume and time of calculations and at the same time the accuracy of the result is very important, The disease can be diagnosed with acceptable accuracy using machine learning algorithms
According to Apnea definition in adults, it is the cessation of breathing for more than 10 seconds and Hypopnea means 50% air circulation reduction from the base during sleep for more than 10 seconds which comes along with arterial oxygen drop more than 4 % or arousal [2]

Summary

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. According to Apnea definition in adults, it is the cessation of breathing for more than 10 seconds and Hypopnea means 50% air circulation reduction from the base during sleep for more than 10 seconds which comes along with arterial oxygen drop more than 4 % or arousal [2]. This disorder appears as central respiratory distress (36%), obstructive apnea (12%), and the combination of abnormal central respiration and obstructive apnea in the rest of the cases [3]. Other symptoms include: High blood pressure, cardiovascular disorders, brain stroke, depression feeling, increasing the risk of driving accidents, lower efficiency and poor performance, lack of motivation and stimulant factor, concentration disability, low tolerance poor endurance and inadequacy in problem-solving [4, 810]

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