Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in
diagnosis, stratification, and management. AI algorithms can help clinicians in the following …

Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …

Concise and unambiguous assessment of a machine learning algorithm is key to classifier
design and performance improvement. In the multi-class classification task, where each …

The PhysioNet/Computing in Cardiology Challenge 2021 focused on the identification of
cardiac abnormalities from electrocardiograms (ECGs) and assessed the diagnostic …

Remote monitoring devices, which can be worn or implanted, have enabled a more effective
healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor …

Abstract Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …

Cardiovascular disease is a major global public health problem, and intelligent diagnostic
approaches play an increasingly important role in the analysis of electrocardiograms …

Cardiac abnormalities are a leading cause of death and their early diagnosis are of
importance for providing timely interventions. The goal of 2020 PhysioNetlCinC challenge …

In data-driven medical research, multi-center studies have long been preferred over single-
center ones due to a single institute sometimes not having enough data to obtain sufficient …

The success of contrastive learning is well known to be dependent on data augmentation.
Although the degree of data augmentations has been well controlled by utilizing pre-defined …