What is it about?

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.

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Why is it important?

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).


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.

Bruno Rodrigues de Oliveira
Pantanal Editora

Read the Original

This page is a summary of: Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection, Computer Methods and Programs in Biomedicine, February 2019, Elsevier,
DOI: 10.1016/j.cmpb.2018.12.028.
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