Computer Methods and Programs in Biomedicine 169 (2019) 59–69 Contents lists available at ScienceDirect Computer Methods and Programs in Biomedicine journal homepage: www.elsevier.com/locate/cmpb Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection Bruno Rodrigues de Oliveira a,∗ , Caio Cesar Enside de Abreu b , Marco Aparecido Queiroz Duarte c , Jozue Vieira Filho d a Department of Electrical Engineering, São Paulo State University (UNESP), Ilha Solteira, Brazil b Department of Computing, Mato Grosso State University (UNEMAT), Alto Araguaia, Brazil c Department of Mathematics, Mato Grosso do Sul State University (UEMS), Cassilândia, Brazil d Telecommunication and Aeronautic Engineering, São Paulo State University (UNESP), São João da Boa Vista, Brazil a r t i c l e i n f o Article history: Received 22 August 2018 Revised 24 November 2018 Accepted 24 December 2018 Keywords: Electrocardiogram analysis Premature Ventricular Contraction Geometrical features a b s t r a c t 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 encompass- ing 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. © 2018 Elsevier B.V. All rights reserved. 1. Introduction The health of an individual is mainly associated to its heart health. There are several diseases that affect the sinus rhythm, which is considered as normal heartbeat, and produce what is called arrhythmia. One of them is the Premature Ventricular Con- traction (PVC), which are premature heartbeats originating from the ventricles. In order to assess the heart health, the most com- mon clinical examinations are those that employ Electrocardio- gram (ECG) analysis. An ECG records the electrical heart activity ∗ Corresponding author. Department of Electrical Engineering, São Paulo State University (UNESP), Brasil Avenue 56, Ilha Solteira, Brazil. E-mail addresses: bruno@cerradosites.com (B.R.d. Oliveira), caio@unemat.br (C.C.E.d. Abreu), marco@uems.br (M.A.Q. Duarte), jozue.vieira@unesp.br (J. Vieira Filho). triggered by the atria and the ventricles, from electrodes placed on the body surface. It is mainly composed by P, Q, R, S, T and U waves, and by PR, ST and QT intervals, according to Fig. 1. P wave and QRS complex represent the atrial and ventricular depo- larization, respectively, and T wave represents the ventricular repo- larization, whereas the atrial repolarization is hidden by the QRS complex due to its low amplitude. The membrane of the cardiac cells has resting and action poten- tial. When the depolarization threshold is overcome the action po- tential is triggered [1], resulting in the atrial and ventricular con- tractions. The PVC is a result of three possible effects: reentry, trig- gered activity and abnormal impulse formation [2]. Although some patients may suffer from PVC episodes without realizing them (e.g. due to external factors such as food and medication), increasing their occurrence frequency can lead to hemodynamic problems [3]. PVC episodes occurrence is common in some patients with heart https://doi.org/10.1016/j.cmpb.2018.12.028 0169-2607/© 2018 Elsevier B.V. All rights reserved.