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

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

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 exacerbati...

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Bibliographic Details
Main Authors: Birmelé, E. (Author), Gonzalez-Bermejo, J. (Author), Lavault, S. (Author), Pegoraro, J.A (Author), Similowski, T. (Author), Wattiez, N. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
adult
Article
breathing rate
chronic obstructive lung disease
Chronic obstructive pulmonary disease (COPD)
Classification
clinical trial
controlled study
exercise
feature extraction
female
human
learning
learning algorithm
machine learning
male
mathematical model
metagenomics
Novelty detection
oxygen therapy
Respiratory pattern
telemonitoring
Telemonitoring
time series analysis
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