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 exacerbation events. Many of these works have been focusing in breathing rate variation, while achieving unsatisfactory sensitivity and/or specificity. This study aims to identify breathing features that better describe respiratory pattern changes in a short-term adjustment of the load-capacity-drive balance, using exercising data.

Results

Under any tested circumstances, breathing rate alone leads to poor capability of classifying rest and effort periods. The best performances were achieved

SUBMITTER: Pegoraro JA 

PROVIDER: S-EPMC8286592 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature