Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Cardiovascular health-related problem is a rapidly increasing integrated field concerning the
processing and fetching the information from cardiovascular systems for early detection and …

Current wavelet thresholding methods for cardiogram signals captured by flexible wearable
sensors face a challenge in achieving both accurate thresholding and real-time signal …

Background Electrocardiogram (ECG) signals are inevitably contaminated with various
kinds of noises during acquisition and transmission. The presence of noises may produce …

Atrial fibrillation and ventricular fibrillation are the two most common cardiac arrhythmia.
These cardiac arrhythmias cause heart strokes and other heart complications leading to an …

The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms
in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and …

Wearable sensing devices (WSDs) have enormous promise for monitoring construction
worker safety. They can track workers and send safety-related information in real time …

Context Epilepsy, characterized by recurrent seizures, is a chronic brain disease that affects
approximately 50 million. Recurrent seizures characterize it. A seizure, a burst of …

Electrocardiogram (ECG) stands as a pivotal non-invasive technique utilized for the
diagnosis of heart diseases. Deep learning methodologies are progressively being …

The joint time‐frequency analysis method represents a signal in both time and frequency.
Thus, it provides more information compared to other one‐dimensional methods. Several …