title: ECG-based heartbeat classification for arrhythmia detection: A survey.
abstract: An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.
authors.affiliation: Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, Brazil. Electronic address: eduluz@iceb.ufop.br.
authors.forename: Eduardo José da S
authors.initials: EJ
authors.lastname: Luz
authors.affiliation: Universidade Federal de Minas Gerais, Computer Science Department, Belo Horizonte, MG, Brazil. Electronic address: william@dcc.ufmg.br.
authors.forename: William Robson
authors.initials: WR
authors.lastname: Schwartz
authors.affiliation: Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, Brazil. Electronic address: guillermo@iceb.ufop.br.
authors.forename: Guillermo
authors.initials: G
authors.lastname: Cámara-Chávez
authors.affiliation: Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, Brazil; Universidade Federal do Paraná, Department of Informatics, Curitiba, PR, Brazil. Electronic address: menotti@iceb.ufop.br.
authors.forename: David
authors.initials: D
authors.lastname: Menotti
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D001331:Automation; D004562:Electrocardiography; D006339:Heart Rate; D006801:Humans; D011795:Surveys and Questionnaires
keywords: ECG-based signal processing; Feature extraction; Heartbeat classification; Heartbeat segmentation; Learning algorithms; Preprocessing