Electrocardiogram (ECG) signal quality assessment (SQA) plays a vital role in significantly
improving the diagnostic accuracy and reliability of unsupervised ECG analysis systems. In …

Purpose Several artificial intelligence (AI) models for the detection and prediction of
cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have …

Electrocardiogram (ECG) can reflect the state of human heart and is widely used in clinical
cardiac examination. However, the electrocardiogram signal is very weak, the anti …

Current electrocardiogram (ECG) signal quality assessment studies have aimed to provide a
two-level classification: clean or noisy. However, clinical usage demands more specific …

Objective: Automatic detection and classification of noises can play a vital role in the
development of robust unsupervised electrocardiogram (ECG) analysis systems. This paper …

In this paper, we propose a new automated quality-aware electrocardiogram (ECG) beat
classification method for effective diagnosis of ECG arrhythmias under unsupervised …

A systematical evaluation work was performed on ten widely used and high‐efficient QRS
detection algorithms in this study, aiming at verifying their performances and usefulness in …

Objective: QRS detection algorithms are needed to analyze electrocardiogram (ECG)
recordings generated in telehealth environments. However, the numerous published QRS …

Electrocardiogram (ECG) acquired by wearable devices is increasingly used for healthcare
applications. However, the ECG signals are severely corrupted by various noises (eg …

Abstract Background and Objectives The presence of noise sources could reduce the
diagnostic capability of the ECG signal and result in inappropriate treatment decisions. To …