Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers
Use este enlace para citar
http://hdl.handle.net/2183/34480
A non ser que se indique outra cousa, a licenza do ítem descríbese como Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC-BY-NC-ND 4.0)
Coleccións
- GI-VARPA - Artigos [79]
Metadatos
Mostrar o rexistro completo do ítemTítulo
Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiersData
2019-01Cita bibliográfica
Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomedical Signal Processing and Control, 47, 41–48. doi:10.1016/j.bspc.2018.08.007
É version de
https://doi.org/10.1016/j.bspc.2018.08.007
Resumo
[Abstract]: A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art.
Palabras chave
Electrocardiogram (ECG)
Heartbeat classification
Support vector machine (SVM)
Combining classifiers
Ensemble of classifiers
Heartbeat classification
Support vector machine (SVM)
Combining classifiers
Ensemble of classifiers
Descrición
©2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). “Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers” has been accepted for publication in Biomedical Signal Processing and Control, 47, 41–48. The Version of Record is available online at: https://doi.org/10.1016/j.bspc.2018.08.007.
Versión do editor
Dereitos
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC-BY-NC-ND 4.0)
ISSN
1746-8094