The soaring popularity of smart devices equipped with electrocardiograms (ECG) is driving a
nationwide craze for predicting heart abnormalities. Smart ECG monitoring system has …

In this study, a weighted federated learning approach is proposed for electrocardiogram
(ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data …

Modern methods in artificial intelligence perform very well on many healthcare datasets, at
times outperforming trained doctors. However, many assumptions made in model training …

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

In this paper, a distributed arrhythmia detection algorithm based on electrocardiogram
(ECG) is proposed for auxiliary diagnosis and treatment. ECG that contains tremendous …

Health monitoring devices are integral parts of smart health in the era of smart connected
communities. In recent years, remote heart monitoring systems are developed to harness …

One of the deadliest diseases, heart disease, claims millions of lives every year worldwide.
The biomedical data collected by health service providers (HSPs) contain private …

Deep learning-based intelligent electrocardiogram (ECG) diagnosis algorithms heavily rely
on large annotated datasets. Unfortunately, in the context of ECG diagnosis, privacy issues …

Electrocardiogram (ECG) signals convey immense information that, when properly
processed, can be used to diagnose various health conditions including arrhythmia and …

Background: Real-world medical data, such as electrocardiogram (ECG), often show a long-
tail distribution and severe category imbalance, and severely imbalanced data generate …