Percorrer por autor "Paiva, Daniela"
A mostrar 1 - 1 de 1
Resultados por pΓ‘gina
Opçáes de ordenação
- Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysisPublication . Ribeiro, Pedro; SΓ‘, Joana; Paiva, Daniela; Rodrigues, Pedro MiguelBackground: 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 π΄πππ’ππππ¦ discrimination results ranged between 73% and 100%, the π πππππ between 68% and 100%, and the π΄ππΆ 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.