Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO

Scientific Reports. 2017;7(1):1-11 DOI 10.1038/s41598-017-09837-3

 

Journal Homepage

Journal Title: Scientific Reports

ISSN: 2045-2322 (Online)

Publisher: Nature Publishing Group

LCC Subject Category: Medicine | Science

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Gabriel Garcia (Vale Institute Of Technology)
Gladston Moreira (Universidade Federal de Ouro Preto, Computing Department)
David Menotti (Universidade Federal do ParanĂ¡, Department of Informatics)
Eduardo Luz (Universidade Federal de Ouro Preto, Computing Department)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 20 weeks

 

Abstract | Full Text

Abstract Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.