A review of arrhythmia detection based on electrocardiogram with artificial intelligence. (3rd July 2022)

• Record Type: Journal Article
• Authors: Liu, Jinlei; Li, Zhiyuan; Jin, Yanrui; Liu, Yunqing; Liu, Chengliang; Zhao, Liqun; Chen, Xiaojun
• Is Part Of: Expert review of medical devices. Volume 19:Number 7(2022)
• Page Start: 549
• Page End: 560
• Publication Date: 2022-07-03
• Subjects: Arrhythmia -- deep learning -- electrocardiogram -- heartbeat classification -- machine learning -- signal quality assessment -- wave delineation; Medical instruments and apparatus -- Periodicals; 610.28
• DOI: 10.1080/17434440.2022.2115887
• Languages: English
• ISSNs: 1743-4440

Abstract

ABSTRACT: Introduction: With the widespread availability of portable electrocardiogram (ECG) devices, there will be a surge in ECG diagnoses. Traditional computer-aided diagnosis of arrhythmia mainly relies on the rules of medical knowledge, which are insufficient due to the limitations of data quality and human expert knowledge. The research of arrhythmia detection methods based on artificial intelligence (AI) techniques can assist physicians in high-precision arrhythmia diagnosis. AI algorithms can also be embedded in smart ECG devices to help more people perform early screening for arrhythmia. Areas covered: The primary objective of this paper is to describe the application of AI methods in the process of arrhythmia detection. Meanwhile, the advantages and limitations of various approaches in different applications are summarized to provide guidance and reference for future research work. Expert opinion: Machine learning (ML) and deep learning (DL) algorithms can be more effectively employed to handle ECG signal denoising and quality assessment, wave detection and delineation, and arrhythmia classification problems. The DL approach can automatically learn deep representation features and temporal features of the ECG signal for heartbeat or rhythm classification. The application of AI methods for arrhythmia detection systems will significantly relieve the pressure on physicians to analyze ECGs.