Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death
globally. Early prediction of CVD's can help in reducing the complications of high-risk …

Mechanical system usually operates in harsh environments, and the monitored vibration
signal faces substantial noise interference, which brings great challenges to the robust fault …

Extracting invariant representations in unlabeled electrocardiogram (ECG) signals is a
challenge for deep neural networks (DNNs). Contrastive learning is a promising method for …

In this article we examined research on using neural architecture search (NAS) in medical
applications, prompted by the current shortage of health care professionals relative to …

Automatic arrhythmia classification has been a cross research hot spot of artificial
intelligence applied on biomedical engineering. In this study, a deep neural network based …

Artificial Intelligence (AI) enabledembedded devices are becoming increasingly important in
the field of healthcarewhere such devices are utilized to assist physicians, clinicians, and …

Cardiovascular disease (CVD) is a most dangerous disease in the world. Early accurate and
automated identification helps the medical professional make a correct diagnosis and …

A language is made up of an infinite/finite number of sentences, which in turn is composed
of a number of words. The Electrocardiogram (ECG) is the most popular noninvasive …

This paper presents a novel machine learning framework for detecting PxAF, a pathological
characteristic of electrocardiogram (ECG) that can lead to fatal conditions such as heart …

The increasing number of vehicles has led to a rise in traffic accidents, with fatigued driving
being a major contributing factor. Bio-electrical signals, particularly electroencephalograms …