Ventricular Fibrillation and Tachycardia Detection from Surface ECG using time-frequency Representation Images as Input Dataset for Machine Learning

Background and objective: To safely select the proper therapy for Ventricullar Fibrillation (VF) is essential to distinct it correctly from Ventricular Tachycardia (VT) and other rhythms. Provided that the required therapy would not be the same, an erroneous detection might lead to serious injuries to the patient or even cause Ventricular Fibrillation (VF). The main novelty of this paper is the use of time-frequency (t-f) representation images as the direct input to the classifier. We hypothesize that this method allow to improve classification results as it allows to eliminate the typical feature selection and extraction stage, and its corresponding loss of information.

Source: Computer Methods and Programs in Biomedicine - February 9, 2017 Category: Bioinformatics Authors: A. Mjahad, A. Rosado-Mu ñoz, M. Bataller-Mompeán, J.V. Francés-Víllora, J.F. Guerrero-Martínez Source Type: research