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

共 19 条

[11]

Moody G.B., 1986, CLIN VALIDATION ECG, V13, P507, DOI DOI 10.1097/00005768-200210000-00019

[12] Reducing motor-vehicle collisions, costs, and fatalities by treating obstructive sleep apnea syndrome [J].

Sassani, A ;

Findley, LJ ;

Kryger, M ;

Goldlust, E ;

George, C ;

Davidson, TM .

SLEEP, 2004, 27 (03) :453-458

[13] Prevalence of obstructive sleep apnea in the general population: A systematic review [J].

Senaratna, Chamara V. ;

Perret, Jennifer L. ;

Lodge, Caroline J. ;

Lowe, Adrian J. ;

Campbell, Brittany E. ;

Matheson, Melanie C. ;

Hamilton, Garun S. ;

Dharmage, Shyamali C. .

SLEEP MEDICINE REVIEWS, 2017, 34 :70-81

[14] Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets plus Neural Networks [J].

Tagluk, M. Emin ;

Sezgin, Necmettin .

JOURNAL OF MEDICAL SYSTEMS, 2010, 34 (06) :1111-1119

[15]

Tamanna S., 2016, Cardiovascular Journal, V8, P143, DOI DOI 10.3329/CARDIO.V8I2.26818

[16]

Thommandram A, 2013, IEEE ENG MED BIO, P5013, DOI 10.1109/EMBC.2013.6610674

[17] Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review [J].

Uddin, M. B. ;

Chow, C. M. ;

Su, S. W. .

PHYSIOLOGICAL MEASUREMENT, 2018, 39 (03)

[18] Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks [J].

Van Steenkiste, Tom ;

Groenendaal, Willemijn ;

Deschrijver, Dirk ;

Dhaene, Tom .

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2019, 23 (06) :2354-2364

[19]

Van Steenkiste T, 2018, IEEE ENG MED BIO, P449, DOI 10.1109/EMBC.2018.8512307

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