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睡眠呼吸暂停低通气综合征的计算机辅助诊断:综述

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review.

作者信息

Alvarez-Estevez Diego, Moret-Bonillo Vicente

机构信息

Sleep Center, Medisch Centrum Haaglanden, 2512 VA The Hague, Netherlands.

Laboratory for Research and Development in Artificial Intelligence (LIDIA), Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain.

出版信息

Sleep Disord. 2015;2015:237878. doi: 10.1155/2015/237878. Epub 2015 Jul 21.

Abstract

Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.

摘要

由于睡眠医学领域的关注度不断提高以及手动诊断睡眠呼吸暂停低通气综合征(SAHS)的相关成本,SAHS的自动诊断已成为一个重要的研究领域。然而,不同技术的增加和异质性使得跟上最新发展有些困难。本文对SAHS计算机辅助诊断领域进行了文献综述,涵盖了该领域过去15年的研究。综述了筛查方法、呼吸事件检测和分类方法、综合诊断系统以及当前商业方法的概述。本文介绍了不同方法的概述,并对当前技术水平进行了验证分析和批判性讨论。

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本文引用的文献

1
Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome.
Open Med Inform J. 2014 Jun 13;8:1-19. doi: 10.2174/1874431101408010001. eCollection 2014.
2
European Data Format Now Supports Video.
Sleep. 2013 Jul 1;36(7):1111. doi: 10.5665/sleep.2822.
3
Apnea-hypopnea index estimation from spectral analysis of airflow recordings.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3444-7. doi: 10.1109/EMBC.2012.6346706.
6
Linear and nonlinear analysis of airflow recordings to help in sleep apnoea-hypopnoea syndrome diagnosis.
Physiol Meas. 2012 Jul;33(7):1261-75. doi: 10.1088/0967-3334/33/7/1261. Epub 2012 Jun 27.
7
Frequently used sleep questionnaires in epidemiological and genetic research for obstructive sleep apnea: a review.
Sleep Med Rev. 2012 Dec;16(6):529-37. doi: 10.1016/j.smrv.2011.12.002. Epub 2012 Mar 17.
8
Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier.
IEEE Trans Inf Technol Biomed. 2012 May;16(3):463-8. doi: 10.1109/TITB.2012.2185809. Epub 2012 Jan 24.
9
Feature selection from nocturnal oximetry using genetic algorithms to assist in obstructive sleep apnoea diagnosis.
Med Eng Phys. 2012 Oct;34(8):1049-57. doi: 10.1016/j.medengphy.2011.11.009. Epub 2011 Dec 6.
10
Automated frequency domain analysis of oxygen saturation as a screening tool for SAHS.
Med Eng Phys. 2012 Sep;34(7):946-53. doi: 10.1016/j.medengphy.2011.10.015. Epub 2011 Dec 3.

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