Deep Learning (DL) has recently become a topic of study in different applications including
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …

The success of deep learning over the traditional machine learning techniques in handling
artificial intelligence application tasks such as image processing, computer vision, object …

This paper introduces a framework for how to appropriately adopt and adjust machine
learning (ML) techniques used to construct electrocardiogram (ECG)-based biometric …

Cardiac disorders are a leading cause of global casualties, emphasizing the need for the
initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram …

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 …

Authentication plays an essential role in securing the communication between sensor nodes
within a wireless body sensor network (WBSN). The electrocardiogram (ECG) as a type of …

Purpose Biometric systems are becoming increasingly important in today's society. The
Electrocardiogram signal proves a suitable contender for such systems thanks to its …

With the advent of various security attacks, biometric authentication methods are gaining
momentum in the security literature. Electrocardiogram or ECG signals are one of the …

Various biometrics such as the face, irises, and fingerprints, which can be obtained in a
relatively simple way in modern society, are used in personal authentication systems to …

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram
(ECG) biometrics is not the exception. However, the algorithm performances rely heavily on …