Machine-learning based feature selection for a non-invasive breathing change detection

Machine-learning based feature selection for a non-invasive breathing change detection

Abstract Background Chronic Obstructive Pulmonary Disease (COPD) is one of the top 10 causes of death worldwide, representing a major public health problem. Researchers have been looking for new technologies and methods for patient monitoring with the intention of an early identification of acute ex...

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Bibliographic Details
Main Authors: Juliana Alves Pegoraro, Sophie Lavault, Nicolas Wattiez, Thomas Similowski, Jésus Gonzalez-Bermejo, Etienne Birmelé
Format: Article
Language:English
Published: BMC 2021-07-01
Series:BioData Mining
Subjects:
Respiratory pattern
Telemonitoring
Classification
Novelty detection
Chronic obstructive pulmonary disease (COPD)
Online Access:https://doi.org/10.1186/s13040-021-00265-8

Internet

https://doi.org/10.1186/s13040-021-00265-8

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