A review of arrhythmia detection based on electrocardiogram with artificial intelligence

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.

KEYWORDS:

• Arrhythmia
• deep learning
• electrocardiogram
• heartbeat classification
• machine learning
• signal quality assessment
• wave delineation

Article highlights

• AI-based methods have achieved excellent results in ECG signal denoising and quality assessment, characteristic wave detection and delineation, and arrhythmia classification.

• The AI-based method of ECG signal quality assessment is of great significance for the intelligent analysis of long-term ECG. DL-based arrhythmia classification methods have surpassed ECG physicians in accuracy.

• The imbalance problem in arrhythmia classification training and the incremental learning problem of massive ECG data need to be further investigated.

Abbreviations

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Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This work was supported by the National Key R&D Program of China (No. 2018YFB1307005) and the Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0102).