Deep learning models have become a popular mode to classify electrocardiogram (ECG)
data. Investigators have used a variety of deep learning techniques for this application …

The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …

Myocardial infarction (MI) accounts for a high number of deaths globally. In acute MI,
accurate electrocardiography (ECG) is important for timely diagnosis and intervention in the …

This article presents a new deep learning approach for cardiac arrhythmia (17 classes)
detection based on long-duration electrocardiography (ECG) signal analysis …

Long-short term memory networks (LSTMs), which have recently emerged in sequential data
analysis, are the most widely used type of recurrent neural networks (RNNs) architecture …

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart.
Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a …

Safety is one of the key requirements for automated vehicles and fault diagnosis is an
effective technique to enhance the vehicle safety. The model-based fault diagnosis method …

Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats.
Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) …

Magnetocardiography (MCG), which uses high-sensitivity magnetometers to record
magnetic field signals generated by electrical activity in the heart, is a noninvasive method …

This paper proposes and tests a sequence-based modeling of deep learning (DL) for
structural damage detection of floating offshore wind turbine (FOWT) blades using Long …