Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection

被引：45

作者：

Pinho, Andre ^{[1
]}

Pombo, Nuno ^{[1
]}

Silva, Bruno M. C. ^{[1
,3
]}

Bousson, Kouamana ^{[2
]}

Garcia, Nuno ^{[1
]}

机构：

[1] Univ Beira Interior, Inst Telecomunicacoes, Covilha, Portugal

[2] Univ Beira Interior, Covilha, Portugal

[3] Univ Europeia, IADE, Av D Carlos I,4, P-1200649 Lisbon, Portugal

来源：

APPLIED SOFT COMPUTING | 2019年 / 83卷

关键词：

Sleep apnea; Electrocardiogram (ECG); Heart rate variability (HRV); ECG-derived respiration (EDR); Feature selection; Classification; Artificial neural network (ANN); Support vector machine (SVM); Linear discriminant analysis (LDA); Partial least squares regression (PLS); Regression analysis (REG); Wiener-Hopf equation (wienerHopf); Augmented naive bayesian classifier (aNBC); Perceptron learning algorithm (PLA); Least mean squares (LMS); ELECTROCARDIOGRAM; CLASSIFICATION;

D O I：

10.1016/j.asoc.2019.105568

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography. (C) 2019 Elsevier B.V. All rights reserved.

引用

页数：10

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