Publication
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis
| dc.contributor.author | Ribeiro, Pedro | |
| dc.contributor.author | Sá, Joana | |
| dc.contributor.author | Paiva, Daniela | |
| dc.contributor.author | Rodrigues, Pedro Miguel | |
| dc.date.accessioned | 2024-01-17T17:01:37Z | |
| dc.date.available | 2024-01-17T17:01:37Z | |
| dc.date.issued | 2024-01-07 | |
| dc.description.abstract | Background: cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. Methods: the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. Results: the 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 discrimination results ranged between 73% and 100%, the 𝑅𝑒𝑐𝑎𝑙𝑙 between 68% and 100%, and the 𝐴𝑈𝐶 between 0.42 and 1. Conclusions: the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/bioengineering11010058 | pt_PT |
| dc.identifier.eid | 85183167121 | |
| dc.identifier.issn | 2306-5354 | |
| dc.identifier.pmid | 38247935 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/43669 | |
| dc.identifier.wos | 001148836000001 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | ECG signals | pt_PT |
| dc.subject | Cardiovascular diseases | pt_PT |
| dc.subject | Machine learning models | pt_PT |
| dc.subject | Discrete wavelet transform | pt_PT |
| dc.subject | Non-linear analysis | pt_PT |
| dc.subject | Discrimination | pt_PT |
| dc.title | Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Bioengineering | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |