In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations

Abstract Aims A pre-operative non-invasive identification of the site of origin (SOO) of outflow tract ventricular arrhythmias (OTVAs) is important to properly plan radiofrequency ablation procedures. Although some algorithms based on electrocardiograms (ECGs) have been developed to predict left vs. right ventricular origins, their accuracy is still limited, especially in complex anatomies. The aim of this work is to use patient-specific electrophysiological simulations of the heart to predict the SOO in OTVA patients. Methods and results An in silico pace-mapping procedure was designed and used on 11 heart geometries, generating for each case simulated ECGs from 12 clinically plausible SOO…

Feature selection using ROC curves on classification problems

Feature Selection (FS) is one of the key stages in classification problems. This paper proposes the use of the area under Receiver Operator Characteristic curves to measure the individual importance of every input as well as a method to discover the variables that yield a statistically significant improvement in the discrimination power of the classification model.

Preface to Data Mining in Biomedical Informatics and Healthcare

Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical Maps

[EN] Optimization of lead placement and interventricular delay settings in patients under cardiac resynchronization therapy is a complex task that might benefit from prior information based on models. Biophysical models can be used to predict the sequence of electrical heart activation in a patient given a set of parameters which should be personalized to the patient. In this paper, we use electroanatomical maps to personalize the endocardial activation of the right ventricle, and the different tissue conductivities in a pig model with left bundle branch block, to reproduce personalized biventricular activations. Following, we tested the personalized heart model by virtually simulating card…

Survival prediction in patients undergoing ischemic cardiopathy

The ischemic cardiopathy is the main cause of death in developed countries. New improved drugs and therapies have appeared last years. However, the interventionist strategy and the most powerful drugs may have complications, and hence, it is very important to know the risk of death associated with patients during their stay in the hospital, or in the next six months. Thus, it is possible to tune the best treatment for each individual patient. In this framework, the use of artificial neural networks is proposed with a double objective: survival prediction and the extraction of the parameters with best predictive capabilities. A cohort of 691 patients treated in the Hospital Clinic, in Barcel…

Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study

[EN] Patients suffering from heart failure and left bundle branch block show electrical ventricular dyssynchrony causing an abnormal blood pumping. Cardiac resynchronization therapy (CRT) is recommended for these patients. Patients with positive therapy response normally present QRS shortening and an increased left ventricle (LV) ejection fraction. However, around one third do not respond favorably. Therefore, optimal location of pacing leads, timing delays between leads and/or choosing related biomarkers is crucial to achieve the best possible degree of ventricular synchrony during CRT application. In this study, computational modeling is used to predict the optimal location and delay of p…