Survivors of myocardial infarction are at risk of life-threatening ventricular tachycardias (VTs)
later in their lives. Current guidelines for implantable cardioverter defibrillators (ICDs) …

Goal: To develop a robust and real-time approach for automatic detection of atrial fibrillation
(AF) in long-term electrocardiogram (ECG) recordings using deep learning (DL). Method: An …

The depth and breadth of data produced in the modern cardiac intensive care unit (CICU)
poses challenges to clinicians and researchers. Artificial intelligence (AI) and machine …

Cardiovascular diseases (CVDs) are a group of heart and blood vessel ailments that can
cause chest pain and trouble breathing, especially while active. However, some patients …

Background Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify
different types of cardiac arrhythmias with the use of a single-lead ECG input data set have …

Background Electrocardiogram (ECG) is a method of recording the electrical activity of the
heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any …

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-
of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning …

New chips for machine learning applications appear, they are tuned for a specific topology,
being efficient by using highly parallel designs at the cost of high power or large complex …

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be
learned to self-extract significant features of the electrocardiogram (ECG) and can generally …

The occurrence of sudden cardiac arrest (SCA) leads to a massive death across the world.
Hence the early prediction of ventricular tachycardia (VT) and ventricular fibrillation (VF) …