Deep Learning (DL) has recently become a topic of study in different applications including
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …

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

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the
accuracy of existing models. Deep Neural Networks (DNNs) are models composed of …

Active researches are currently being performed to incorporate the wealth of scientific
knowledge into data-driven approaches (eg, neural networks) in order to improve the latter's …

Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography
(PPG) could open the way for better sleep disorder screening and health monitoring …

Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and
mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial …

Background Heart disease diagnosis is a challenging task and it is important to explore
useful information from the massive amount of electrocardiogram (ECG) records of patients …

With the rapid development of the Internet of Things (IoT), it becomes convenient to use
mobile devices to remotely monitor the physiological signals (eg, Arrhythmia diseases) of …

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 …