Key result
Artificial intelligence methods, including machine learning and deep learning, can effectively handle ECG signal denoising, wave detection, and arrhythmia classification to assist physicians.
Why the study?
Traditional rule-based computer-aided arrhythmia diagnosis is limited by data quality and human knowledge amid a surge in ECG diagnoses from portable devices.
Artificial intelligence and deep learning algorithms can enhance ECG signal processing and automate arrhythmia classification, potentially improving diagnostic accuracy and reducing physician workload.
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.
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Liu et al. (2022) conducted a review in Arrhythmia. Artificial intelligence (machine learning and deep learning) was evaluated. Artificial intelligence methods, including machine learning and deep learning, can effectively handle ECG signal denoising, wave detection, and arrhythmia classification to assist physicians.
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