Autonomous ships are expected to improve the level of safety and efficiency in future
maritime navigation. Such vessels need perception for two purposes: to perform …

Plant sensor data contain errors that can hamper process analysis and decision-making.
Those dataset are not used to their full potential due to the complexity of their processing …

Intelligently detecting anomalies in health sensor data streams (eg, Electrocardiogram,
ECG) can improve the development of E-health industry. The physiological signals of …

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 advent of advances in machine learning (ML)-based techniques has popularized wide
applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In …

While cardiovascular diseases (CVDs) are commonly diagnosed by cardiologists via
inspecting electrocardiogram (ECG) waveforms, these decisions can be supported by a data …

Goal: Identifying the circulation state during out-of-hospital cardiac arrest (OHCA) is
essential to determine what life-saving therapies to apply. Currently algorithms discriminate …

Feedback on ventilation could help improve cardiopulmonary resuscitation quality and
survival from out-of-hospital cardiac arrest (OHCA). However, current technology that …

Worldwide, the automatic diagnosis (ie, detection and localization) of myocardial infarction
(MI) remains a challenging problem. Clinically, MI is mainly detected and located according …

Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical
and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of …