Publication
COVID-19 activity screening by a smart-data-driven multi-band voice analysis
| dc.contributor.author | Silva, Gabriel | |
| dc.contributor.author | Batista, Patrícia | |
| dc.contributor.author | Rodrigues, Pedro Miguel | |
| dc.date.accessioned | 2023-07-10T09:33:25Z | |
| dc.date.available | 2023-07-10T09:33:25Z | |
| dc.date.issued | 2022-11-15 | |
| dc.description.abstract | COVID-19 is a disease caused by the new coronavirus SARS-COV-2 which can lead to severe respiratory infections. Since its first detection it caused more than six million worldwide deaths. COVID-19 diagnosis non-invasive and low-cost methods with faster and accurate results are still needed for a fast disease control. In this research, 3 different signal analyses have been applied (per broadband, per sub-bands and per broadband & sub-bands) to Cough, Breathing & Speech signals of Coswara dataset to extract non-linear patterns (Energy, Entropies, Correlation Dimension, Detrended Fluctuation Analysis, Lyapunov Exponent & Fractal Dimensions) for feeding a XGBoost classifier to discriminate COVID-19 activity on its different stages. Classification accuracies ranged between 83.33% and 98.46% have been achieved, surpassing the state-of-art methods in some comparisons. It should be empathized the 98.46% of accuracy reached on pair Healthy Controls vs all COVID-19 stages. The results shows that the method may be adequate for COVID-19 diagnosis screening assistance. | pt_PT |
| dc.description.version | info:eu-repo/semantics/acceptedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.jvoice.2022.11.008 | pt_PT |
| dc.identifier.eid | 85143292554 | |
| dc.identifier.issn | 0892-1997 | |
| dc.identifier.pmc | PMC9663738 | |
| dc.identifier.pmid | 36464573 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/41625 | |
| dc.identifier.wos | 001494077600001 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Breathing | pt_PT |
| dc.subject | Classification | pt_PT |
| dc.subject | Cough | pt_PT |
| dc.subject | COVID-19 | pt_PT |
| dc.subject | Non-linear patterns | pt_PT |
| dc.subject | Speech signals | pt_PT |
| dc.title | COVID-19 activity screening by a smart-data-driven multi-band voice analysis | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Journal of Voice | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |