A Multi-level Features Fusion Network for Detecting Obstructive Sleep Apnea Hypopnea Syndrome

被引：0

作者：

Lv, Xingfeng ^{[1
]}

Li, Jinbao ^{[2
]}

机构：

[1] Heilongjiang Univ, Sch Comp Sci & Technol, Harbin, Peoples R China

[2] Qilu Univ Technol, Shandong Artificial Intelligence Inst, Shandong Acad Sci, Jinan, Peoples R China

来源：

ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2020, PT III | 2020年 / 12454卷

关键词：

Obstructive Sleep Apnea Hypopnea Syndrome; Multi-level features fusion network; Deep learning; Deep features; Shallow features;

D O I：

10.1007/978-3-030-60248-2_34

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is the most common sleep disorder. If not treated in time, OSAHS will lead to high blood pressure, heart or cerebrovascular diseases. At present, the commend method is based on deep learning which can extract deep features. But it does not make full use of the influence of shallow features on sleep classification. Therefore, this paper proposes a Multi-Level Features Fusion Network (MLF2N) model to detect OSAHS. MLF2N can learn different levels of features from the different respiratory signals. In this paper, 2056 subjects were classified as Obstructive Sleep Apnea, Hypopnea and Normal sleep on the Multi Ethical Study of Atherosclerosis (MESA) data set using this model. The experimental results show that the accuracy of the three respiratory signals can reach 85.5% through this model.

引用

页码：509 / 519

页数：11

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