Objectives: Deep learning models such as convolutional neural networks (CNNs) have been
applied successfully to medical imaging, but biomedical signal analysis has yet to fully …

Automated analysis of physiological time series is utilized for many clinical applications in
medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural …

This paper provides an extensive literature review focusing on the modeling of artificial
pacemakers and the various mechanisms employed for their pacing control. In this survey …

Deep Learning (DL) has been recently employed to build smart systems that perform
incredibly well in a wide range of tasks, such as image recognition, machine translation, and …

Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias.
This paper investigates the use of machine learning classification algorithms for ECG …

The rapid identification of arousal is of great interest in various applications such as health
care for the elderly, athletes, drivers and students, among others. Therefore, advanced …

The field of biometrics is a pattern recognition problem, where the individual traits are coded,
registered, and compared with other database records. Due to the difficulties in reproducing …

Early detection of stress condition is beneficial to prevent long-term mental illness like
depression and anxiety. This paper introduces an accurate identification of stress/calm …

The production, testing, and processing of signals without any interpretation is a crucial task
with time scale periods in today's biological applications. As a result, the proposed work …

While the global healthcare market of wearable devices has been growing significantly in
recent years and is predicted to reach $60 billion by 2028, many important healthcare …