Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …

Background and objectives: Most deep-learning-related methodologies for
electrocardiogram (ECG) classification are focused on finding an optimal deep-learning …

Developing prediction models for emerging infectious diseases from relatively small
numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 …

Over the last few years, dozens of healthcare surveys have shown a shortage of doctors and
an alarming doctor-population ratio. With the motivation of assisting doctors and utilizing …

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many
laborious and time-consuming processes in hospitals or ambulatory settings, eg home …

Clinical decision making in the emergency room needs to be fast and accurate, especially
for myocardial infarction (MI) cases. The best way to address data-based decisions is …

Clinical diagnosis is a challenging task for which high expertise is required at the doctors'
end. It is recognized that technology integration with the clinical domain would facilitate the …

Supercapacitors are gaining popularity these days because of their good cycle stability,
superior specific capacitance, high power density, and energy density. Herein, we report the …

Machine learning classifiers for detection of abnormal clinical electroencephalography
(EEG) signals have advanced signficantly in recent years, largely supported by the carefully …

Unsupervised deep learning methods for solving audio restoration problems extensively rely
on carefully tailored neural architectures that carry strong inductive biases for defining priors …