Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review

• Published in: Information sciences Vol. 467; pp. 99 - 114
• Main Authors: Hagiwara, Yuki, Fujita, Hamido, Oh, Shu Lih, Tan, Jen Hong, Tan, Ru San, Ciaccio, Edward J, Acharya, U Rajendra
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2018
• Subjects: Arrhythmia; Atrial fibrillation; Computer-aided diagnosis system; Electrocardiogram signals; Machine learning; Computer-aided diagnosis system; Electrocardiogram signals; Arrhythmia; Atrial fibrillation; Machine learning
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2018.07.063

•Existing AF detection techniques are discussed.•Building blocks of CADx system are described.•Different features explored by researchers are presented.•State-of-art CADx system for AF are highlighted. Arrhythmia is a type of disorder that affects the pattern and rate of the heartbeat. Among the various arrhythmia conditions, atrial fibrillation (AF) is the most prevalent. AF is associated with a chaotic, and frequently fast, heartbeat. Moreover, AF increases the risk of cardioembolic stroke and other heart-related problems such as heart failure. Thus, it is necessary to screen for AF and receive proper treatment before the condition progresses. To date, electrocardiogram (ECG) feature analysis is the gold standard for the diagnosis of AF. However, because it is time-varying, AF ECG signals are difficult to interpret. The ECG signals are often contaminated with noise. Further, manual interpretation of ECG signals may be subjective, time-consuming, and susceptible to inter-observer variabilities. Various computer-aided diagnosis (CADx) methods have been proposed to remedy these shortcomings. In this paper, different CADx systems developed by researchers are discussed. Also, the potentials of the CADx system are highlighted. [Display omitted]