Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection

• Autor(a) principal: Oliveira, Bruno Rodrigues de [UNESP]
• Data de Publicação: 2019
• Outros Autores: Abreu, Caio Cesar Enside de, Duarte, Marco Aparecido Queiroz, Vieira Filho, Jozue [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.cmpb.2018.12.028; http://hdl.handle.net/11449/189987
• topic: Electrocardiogram analysis; Geometrical features; Premature Ventricular Contraction

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.