Affiliations: [a] School of Software, Nanchang University, Nanchang, Jiangxi, China | [b] Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
Correspondence:
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Corresponding author: Fen Wang, Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China. E-mail: 393370147@qq.com; Linglin Xia, School of Software, Nanchang University, Nanchang, Jiangxi, China. E-mail: xialinglin@ncu.edu.cn.
Note: [1] These authors contributed equally to this work.
Abstract: BACKGROUND: Obstructive sleep apnea (OSA) is a common sleep disordered breathing disorder, which can cause serious damage to multiple human systems. Although polysomnography (PSG) is the current gold standard for diagnosis, it is complex and expensive. Therefore, it is of great significance to find a simple, economical and rapid primary screening and diagnosis method to replace PSG for the diagnosis of OSA. OBJECTIVE: The purpose of this study is to propose a new method for the diagnosis and classification of OSA, which is used to automatically detect the duration of sleep apnea hypopnea events (AHE), so as to estimate the ratio(S) of the total duration of all-night AHE to the total sleep time only based on the sound signal of sleep respiration, and to identify OSA. METHODS: We performed PSG tests on participants and extracted relevant sleep breathing sound signal data. This study is carried out in two stages. In the first stage, the relevant PSG report data of eligible subjects were recorded, the total duration of AHE in each subject’s data was extracted, and the S value was calculated to evaluate the severity of OSA. In the second stage, only the sleep breath sound signal data of the same batch of subjects were used for automatic detection, and the S value in the sleep breath sound signal was extracted, and the S value was compared with the PSG diagnosis results to calculate the accuracy of the experimental method. RESULTS: Among 225 subjects. Using PSG as the reference standard, the S value extracted from the PSG diagnostic data report can accurately diagnose OSA(accuracy rate 99.56%) and distinguish its severity (accuracy rate 95.11%). The accuracy of the S value detected in the sleep breathing sound signal in the diagnosis of severe OSA reached 100%. CONCLUSION: The results show that the experimental parameter S value is feasible in OSA diagnosis and classification. OSA can be identified and evaluated only by sleep breathing sounds. This method helps to simplify the diagnostic grading of traditional OSA and lays a foundation for the subsequent development of simple diagnostic grading equipment.
Keywords: Obstructive sleep apnea, sound, Duration, diagnosis and classification
