ECG-based heartbeat classification for arrhythmia detection: A survey. Issue 127 (April 2016)

• Record Type: Journal Article
• Authors: Luz, Eduardo José da S.; Schwartz, William Robson; Cámara-Chávez, Guillermo; Menotti, David
• Is Part Of: Computer methods and programs in biomedicine. Issue 127(2016)
• Page Start: 144
• Page End: 164
• Publication Date: 2016-04
• Subjects: ECG-based signal processing -- Heartbeat classification -- Preprocessing -- Heartbeat segmentation -- Feature extraction -- Learning algorithms; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.12.008
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : Highlights: Surveys the feature description methods, and the learning algorithms employed. Also surveys the ECG signal preprocessing and the heartbeat segmentation techniques. Description of databases used for methods evaluation indicated by the AAMI standard. Discussion of limitations and drawbacks of the methods in the literature. Concluding remarks and future challenges are also pointed out. 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.