In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …

Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …

Industry 4.0 in healthcare involves use of a wide range of modern technologies including
digitisation, artificial intelligence, user response data (ergonomics), human psychology, the …

Background Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple
heart abnormalities that covers a wide range of arrhythmias, with better-than-human …

Disease diagnosis and monitoring using conventional healthcare services is typically
expensive and has limited accuracy. Wearable health technology based on flexible …

Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural
networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer …

Cardiovascular disease (CVD), despite the significant advances in the diagnosis and
treatments, still represents the leading cause of morbidity and mortality worldwide. In order …

The success of deep learning over the traditional machine learning techniques in handling
artificial intelligence application tasks such as image processing, computer vision, object …

In clinical practice, one patient may suffer from more than one arrhythmia simultaneously,
that is, one ECG record may be associated with multiple types of arrhythmias. In fact, there …