Publication
Assessment of the post-acute covid-19 syndrome cardiovascular effect through ECG analysis
| dc.contributor.author | Ribeiro, P. | |
| dc.contributor.author | Souza, C. C. C. D. | |
| dc.contributor.author | Camerino, C. M. C. | |
| dc.contributor.author | Pordeus, D. | |
| dc.contributor.author | Leite, C. F. | |
| dc.contributor.author | Marques, J. A. L. | |
| dc.contributor.author | Madeiro, J. P. | |
| dc.contributor.author | Rodrigues, P. M. | |
| dc.date.accessioned | 2025-05-21T17:31:44Z | |
| dc.date.available | 2025-05-21T17:31:44Z | |
| dc.date.issued | 2024-10-11 | |
| dc.description.abstract | Introduction: SARS-CoV-2, a virus responsible for the emergence of the life-threatening disease known as COVID-19, exhibits a diverse range of clinical manifestations. The spectrum of symptoms varies widely, encompassing mild to severe presentations, while a considerable portion of the population remains asymptomatic. COVID-19, primarily a respiratory virus, has been linked to cardiovascular complications in some patients. Notably, cardiac issues can also arise after recovery, contributing to post-acute COVID-19 syndrome, a significant concern for patient health. The present study intends to evaluate the post-acute COVID-19 syndrome cardiovascular effect through ECG by comparing patients affected with cardiac diseases without COVID-19 diagnosis report (class 1) and patients with cardiac pathologies who present post-acute COVID-19 syndrome (class 2). Methods: From 2 body positions, a total of 10 non-linear features, extracted every 1 second under a multi-band analysis performed by Discrete Wavelet Transform (DWT), have been compressed by 6 statistical metrics to serve as inputs for an individual feature analysis by the means of Mann-Whitney U-test and XROC classification. Results and Discussion: 480 Mann-Whitney U-test statistical analyses and XROC discrimination approaches have been done. The percentage of statistical analysis with significant differences (p<0.05) was 30.42% (146 out of 480). The best overall results were obtained by approximating the feature Energy, with the data compressor Kurtosis in the body position Down. Those results were 83.33% of Accuracy, 83.33% of Sensitivity, 83.33% of Specificity and 87.50% of AUC. Conclusions: The results show that the applied methodology can be a way to show changes in cardiac behaviour provoked by post-acute COVID-19 syndrome. | eng |
| dc.identifier.uri | http://hdl.handle.net/10400.14/53369 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.rights.uri | N/A | |
| dc.subject | Covid-19 | |
| dc.subject | ECG | |
| dc.subject | Multi-band analysis | |
| dc.subject | Classification | |
| dc.subject | Statistical analysis | |
| dc.title | Assessment of the post-acute covid-19 syndrome cardiovascular effect through ECG analysis | eng |
| dc.type | report | |
| dspace.entity.type | Publication | |
| oaire.citation.title | The 1st International Online Conference on Bioengineering | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |