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首页> 外文期刊>Biocybernetics and biomedical engineering >Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals
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Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals

机译：使用HRV和EDR信号在睡眠呼吸暂停自动检测的内在带功能技术的应用

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摘要

Sleep apnea is the most common sleep disorder that causes respiratory, cardiac and brain diseases. The heart rate variability (HRV) and the electrocardiogram-derived respiration (EDR) signals to capture the cardio-respiratory information and the features extracted from these two signals have been used for the detection of sleep apnea. Detection of sleep apnea using the combination of HRV and EDR signals may provide more information. This paper proposes a novel method for the automated detection of sleep apnea based on the features extracted from HRV and EDR signals. The method involves the extraction of features from the intrinsic band functions (IBFs) of both EDR and HRV signals, and the classification using kernel extreme learning machine (KELM). The IBFs of HRV and EDR signals are evaluated using the Fourier decomposition method (FDM). The energy and the fuzzy entropy (FE) features are extracted from these IBFs. The kernel extreme learning machine (KELM) classifier with four kernel functions such as 'linear', 'polynomial', 'radial basis function (RBF)' and 'cosine wavelet kernel' is used for the automated detection of sleep apnea. The proposed technique yielded a sensitivity and a specificity of 78.02% and 74.64%, respectively using the public database. The method outperformed some of the reported works using HRV and EDR signals. (C) 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
机译：睡眠呼吸暂停是最常见的睡眠障碍，导致呼吸，心脏和脑疾病。心率变异性（HRV）和心电图衍生的呼吸（EDR）信号以捕获心血管呼吸信息和从这两个信号中提取的特征已经用于检测睡眠呼吸暂停。使用HRV和EDR信号的组合检测睡眠呼吸暂停可以提供更多信息。本文提出了一种基于从HRV和EDR信号提取的特征自动检测睡眠呼吸暂停的新方法。该方法涉及从EDR和HRV信号的内在频带功能（IBF）的提取特征，以及使用内核极端学习机（KELM）的分类。使用傅里叶分解方法（FDM）评估HRV和EDR信号的IBF。能量和模糊熵（FE）特征是从这些IBF中提取的。内核极端学习机（KELM）分类器具有四个内核功能，如“线性”，“多项式”，“径向基函数（RBF）”和“余弦小波核”用于睡眠呼吸暂停的自动检测。使用公共数据库，所提出的技术分别产生了敏感性和78.02％和74.64％的特异性。该方法使用HRV和EDR信号表现出一些报告的作品。 （c）2017年纳雷斯州纳雷斯省生物庭院研究所和波兰科学院生物医学工程。 elsevier b.v出版。保留所有权利。

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