Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection
Nenhuma Miniatura disponível
Data
2019-02-01
Autores
Oliveira, Bruno Rodrigues de [UNESP]
Abreu, Caio Cesar Enside de
Duarte, Marco Aparecido Queiroz
Vieira Filho, Jozue [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
Background and Objective: Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices is often used and computational tools can provide essential assistance to specialists. This paper presents a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes (Q, R and S waves). Methods: Initially, a preprocessing stage based on wavelet denoising electrocardiogram signal scaling is applied. Then, the signal is segmented taking into account the ventricular depolarization timing and a new set of geometrical features are extracted. In order to validate this approach, simulations encompassing eight different classifiers are presented. To select the best classifiers, a new methodology is proposed based on the Analytic Hierarchy Process. Results: The best results, achieved with an Artificial Immune System, were 98.4%, 91.1% and 98.7% for accuracy, sensitivity and specificity, respectively. When artificial examples were generated to balance the dataset, the recognition performance increased to 99.0%, 98.5% and 99.5%, employing the Support Vector Machine classifier. Conclusions: The proposed approach is compared with some of latest references and results indicate its effectiveness as a method for recognizing premature ventricular contraction. Besides, the overall system presents low computation load.
Descrição
Palavras-chave
Electrocardiogram analysis, Geometrical features, Premature Ventricular Contraction
Como citar
Computer Methods and Programs in Biomedicine, v. 169, p. 59-69.