Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field
and has recently received significant interest as a promising biometric trait. However, ECG …

In this paper, a novel computationally intelligent-based electrocardiogram (ECG) signal
classification methodology using a deep learning (DL) machine is developed. The focus is …

Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising
reliable recognition in diverse applications. Commercial products using these traits for …

Electrocardiogram signals acquired through a steering wheel could be the key to seamless,
highly comfortable, and continuous human recognition in driving settings. This paper …

This study focuses on using EEG signal-based behavioral biometric data to classify and
identify persons. A person identification system based on a nonlinear model with excellent …

We propose a practical system design for biometrics authentication based on
electrocardiogram (ECG) signals collected from mobile or wearable devices. The ECG …

Electrical biosignals are favored as biometric traits due to their hidden nature and allowing
for liveness detection. This study explored the feasibility of surface electromyogram (sEMG) …

In this paper, a novel electrocardiogram (ECG) signal classification and patient screening
method is developed. The focus is on identifying patients with paroxysmal atrial fibrillation …

Aiming towards increased robustness to noise and variability, this paper proposes a novel
method for electrocardiogram-based authentication, based on an end-to-end convolutional …

Background: Cardiovascular diseases (CVD) are the leading cause of death globally.
Electrocardiogram (ECG) analysis can provide thoroughly assessment for different CVDs …