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

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 Rat...

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Bibliographic Details
Main Author: Pinho, André (author)
Other Authors: Pombo, Nuno (author), Silva, Bruno M.C. (author), Bousson, K. (author), Garcia, Nuno M. (author)
Format: article
Language:eng
Published: 2020
Subjects:
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
Online Access:http://hdl.handle.net/10400.6/8254
Country:Portugal
Oai:oai:ubibliorum.ubi.pt:10400.6/8254

Internet

http://hdl.handle.net/10400.6/8254

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