An electrocardiogram (ECG) records the electrical signal from the heart to check for different
heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre …

Physiological and pathological information within electrocardiogram (ECG) is crucial for the
diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn …

In recent years, with the rapid development of artificial intelligence, human activity
recognition has become a research focus. The complex, dynamic and variable features of …

Millions of people around the world are affected by arrhythmias, which are abnormal
activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can …

This paper proposes a denoising method based on an adversarial deep learning approach
for the post-processing of multi-channel fetal electrocardiogram (ECG) signals. As it's well …

Bipolar disorder is a serious psychiatric disorder characterized by periodic episodes of
manic and depressive symptomatology. Due to the high percentage of people suffering from …

Motor imagery brain-computer interface (BCI) systems require accurate and fast recognition
of brain activity patterns for reliable communication and interaction. Achieving this accuracy …

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 …

Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and
prevent cardiovascular diseases in time. Many classification approaches have been …

Heartbeat classification plays an important role in identifying cardiac arrhythmias. Although
automated heartbeat classification approaches have been broadly reported, they still suffer …